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Created January 9, 2019 22:51
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Kickstarter_project.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Kickstarter Campaigns\n",
"## An analysis with interactive visualizations\n",
"\n",
"### Here are the questions answered below:\n",
"1. What percentage of campaign succeed or fail?\n",
"2. How many campaigns per year have there been on Kickstarter since 2009?\n",
"3. How many campaigns in each category have there been on Kickstarter since 2009 per year? and how many were successful?\n",
"4. How many total backers per category since 2009? (credence to the crowd-sourcing claim)\n",
"5. What is the average run time per campaign? How quickly do you know if your campaign will succeed or fail?\n",
"6. How global is the Kickstarter community? How much has each country contributed?\n",
"7. Which campaign categories are more popular during which months? and which campaign categories raise more money during which months?\n",
"\n",
"#### Data: https://www.kaggle.com/kemical/kickstarter-projects"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
],
"text/vnd.plotly.v1+html": [
"<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from time import time\n",
"\n",
"from IPython.display import display # Allows the use of display() for DataFrames\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"# Pretty display for notebooks\n",
"%matplotlib inline\n",
"\n",
"import datetime\n",
"import pycountry\n",
"\n",
"import plotly.tools as tls\n",
"import plotly.offline as py\n",
"from plotly.offline import init_notebook_mode, iplot, plot\n",
"import plotly.graph_objs as go\n",
"init_notebook_mode(connected=True)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ID</th>\n",
" <th>name</th>\n",
" <th>category</th>\n",
" <th>main_category</th>\n",
" <th>currency</th>\n",
" <th>deadline</th>\n",
" <th>goal</th>\n",
" <th>launched</th>\n",
" <th>pledged</th>\n",
" <th>state</th>\n",
" <th>backers</th>\n",
" <th>country</th>\n",
" <th>usd pledged</th>\n",
" <th>usd_pledged_real</th>\n",
" <th>usd_goal_real</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1000002330</td>\n",
" <td>The Songs of Adelaide &amp; Abullah</td>\n",
" <td>Poetry</td>\n",
" <td>Publishing</td>\n",
" <td>GBP</td>\n",
" <td>2015-10-09</td>\n",
" <td>1000.0</td>\n",
" <td>2015-08-11 12:12:28</td>\n",
" <td>0.0</td>\n",
" <td>failed</td>\n",
" <td>0</td>\n",
" <td>GB</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1533.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1000003930</td>\n",
" <td>Greeting From Earth: ZGAC Arts Capsule For ET</td>\n",
" <td>Narrative Film</td>\n",
" <td>Film &amp; Video</td>\n",
" <td>USD</td>\n",
" <td>2017-11-01</td>\n",
" <td>30000.0</td>\n",
" <td>2017-09-02 04:43:57</td>\n",
" <td>2421.0</td>\n",
" <td>failed</td>\n",
" <td>15</td>\n",
" <td>US</td>\n",
" <td>100.0</td>\n",
" <td>2421.0</td>\n",
" <td>30000.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1000004038</td>\n",
" <td>Where is Hank?</td>\n",
" <td>Narrative Film</td>\n",
" <td>Film &amp; Video</td>\n",
" <td>USD</td>\n",
" <td>2013-02-26</td>\n",
" <td>45000.0</td>\n",
" <td>2013-01-12 00:20:50</td>\n",
" <td>220.0</td>\n",
" <td>failed</td>\n",
" <td>3</td>\n",
" <td>US</td>\n",
" <td>220.0</td>\n",
" <td>220.0</td>\n",
" <td>45000.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1000007540</td>\n",
" <td>ToshiCapital Rekordz Needs Help to Complete Album</td>\n",
" <td>Music</td>\n",
" <td>Music</td>\n",
" <td>USD</td>\n",
" <td>2012-04-16</td>\n",
" <td>5000.0</td>\n",
" <td>2012-03-17 03:24:11</td>\n",
" <td>1.0</td>\n",
" <td>failed</td>\n",
" <td>1</td>\n",
" <td>US</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>5000.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1000011046</td>\n",
" <td>Community Film Project: The Art of Neighborhoo...</td>\n",
" <td>Film &amp; Video</td>\n",
" <td>Film &amp; Video</td>\n",
" <td>USD</td>\n",
" <td>2015-08-29</td>\n",
" <td>19500.0</td>\n",
" <td>2015-07-04 08:35:03</td>\n",
" <td>1283.0</td>\n",
" <td>canceled</td>\n",
" <td>14</td>\n",
" <td>US</td>\n",
" <td>1283.0</td>\n",
" <td>1283.0</td>\n",
" <td>19500.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ID name \\\n",
"0 1000002330 The Songs of Adelaide & Abullah \n",
"1 1000003930 Greeting From Earth: ZGAC Arts Capsule For ET \n",
"2 1000004038 Where is Hank? \n",
"3 1000007540 ToshiCapital Rekordz Needs Help to Complete Album \n",
"4 1000011046 Community Film Project: The Art of Neighborhoo... \n",
"\n",
" category main_category currency deadline goal \\\n",
"0 Poetry Publishing GBP 2015-10-09 1000.0 \n",
"1 Narrative Film Film & Video USD 2017-11-01 30000.0 \n",
"2 Narrative Film Film & Video USD 2013-02-26 45000.0 \n",
"3 Music Music USD 2012-04-16 5000.0 \n",
"4 Film & Video Film & Video USD 2015-08-29 19500.0 \n",
"\n",
" launched pledged state backers country usd pledged \\\n",
"0 2015-08-11 12:12:28 0.0 failed 0 GB 0.0 \n",
"1 2017-09-02 04:43:57 2421.0 failed 15 US 100.0 \n",
"2 2013-01-12 00:20:50 220.0 failed 3 US 220.0 \n",
"3 2012-03-17 03:24:11 1.0 failed 1 US 1.0 \n",
"4 2015-07-04 08:35:03 1283.0 canceled 14 US 1283.0 \n",
"\n",
" usd_pledged_real usd_goal_real \n",
"0 0.0 1533.95 \n",
"1 2421.0 30000.00 \n",
"2 220.0 45000.00 \n",
"3 1.0 5000.00 \n",
"4 1283.0 19500.00 "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Load the dataset\n",
"ks_data = pd.read_csv(\"ks-projects-201801.csv\")\n",
"\n",
"# Display the first records\n",
"display(ks_data.head(n=5))"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of columns: 15\n",
"Number of rows: 378661\n"
]
}
],
"source": [
"#check the shape of the data set\n",
"num_rows, num_cols = ks_data.shape\n",
"print('Number of columns: {}'.format(num_cols))\n",
"print('Number of rows: {}'.format(num_rows))"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ID</th>\n",
" <th>goal</th>\n",
" <th>pledged</th>\n",
" <th>backers</th>\n",
" <th>usd pledged</th>\n",
" <th>usd_pledged_real</th>\n",
" <th>usd_goal_real</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>3.786610e+05</td>\n",
" <td>3.786610e+05</td>\n",
" <td>3.786610e+05</td>\n",
" <td>378661.000000</td>\n",
" <td>3.748640e+05</td>\n",
" <td>3.786610e+05</td>\n",
" <td>3.786610e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1.074731e+09</td>\n",
" <td>4.908079e+04</td>\n",
" <td>9.682979e+03</td>\n",
" <td>105.617476</td>\n",
" <td>7.036729e+03</td>\n",
" <td>9.058924e+03</td>\n",
" <td>4.545440e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>6.190862e+08</td>\n",
" <td>1.183391e+06</td>\n",
" <td>9.563601e+04</td>\n",
" <td>907.185035</td>\n",
" <td>7.863975e+04</td>\n",
" <td>9.097334e+04</td>\n",
" <td>1.152950e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>5.971000e+03</td>\n",
" <td>1.000000e-02</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>1.000000e-02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>5.382635e+08</td>\n",
" <td>2.000000e+03</td>\n",
" <td>3.000000e+01</td>\n",
" <td>2.000000</td>\n",
" <td>1.698000e+01</td>\n",
" <td>3.100000e+01</td>\n",
" <td>2.000000e+03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1.075276e+09</td>\n",
" <td>5.200000e+03</td>\n",
" <td>6.200000e+02</td>\n",
" <td>12.000000</td>\n",
" <td>3.947200e+02</td>\n",
" <td>6.243300e+02</td>\n",
" <td>5.500000e+03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1.610149e+09</td>\n",
" <td>1.600000e+04</td>\n",
" <td>4.076000e+03</td>\n",
" <td>56.000000</td>\n",
" <td>3.034090e+03</td>\n",
" <td>4.050000e+03</td>\n",
" <td>1.550000e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>2.147476e+09</td>\n",
" <td>1.000000e+08</td>\n",
" <td>2.033899e+07</td>\n",
" <td>219382.000000</td>\n",
" <td>2.033899e+07</td>\n",
" <td>2.033899e+07</td>\n",
" <td>1.663614e+08</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ID goal pledged backers usd pledged \\\n",
"count 3.786610e+05 3.786610e+05 3.786610e+05 378661.000000 3.748640e+05 \n",
"mean 1.074731e+09 4.908079e+04 9.682979e+03 105.617476 7.036729e+03 \n",
"std 6.190862e+08 1.183391e+06 9.563601e+04 907.185035 7.863975e+04 \n",
"min 5.971000e+03 1.000000e-02 0.000000e+00 0.000000 0.000000e+00 \n",
"25% 5.382635e+08 2.000000e+03 3.000000e+01 2.000000 1.698000e+01 \n",
"50% 1.075276e+09 5.200000e+03 6.200000e+02 12.000000 3.947200e+02 \n",
"75% 1.610149e+09 1.600000e+04 4.076000e+03 56.000000 3.034090e+03 \n",
"max 2.147476e+09 1.000000e+08 2.033899e+07 219382.000000 2.033899e+07 \n",
"\n",
" usd_pledged_real usd_goal_real \n",
"count 3.786610e+05 3.786610e+05 \n",
"mean 9.058924e+03 4.545440e+04 \n",
"std 9.097334e+04 1.152950e+06 \n",
"min 0.000000e+00 1.000000e-02 \n",
"25% 3.100000e+01 2.000000e+03 \n",
"50% 6.243300e+02 5.500000e+03 \n",
"75% 4.050000e+03 1.550000e+04 \n",
"max 2.033899e+07 1.663614e+08 "
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ks_data.describe()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 378661 entries, 0 to 378660\n",
"Data columns (total 15 columns):\n",
"ID 378661 non-null int64\n",
"name 378657 non-null object\n",
"category 378661 non-null object\n",
"main_category 378661 non-null object\n",
"currency 378661 non-null object\n",
"deadline 378661 non-null object\n",
"goal 378661 non-null float64\n",
"launched 378661 non-null object\n",
"pledged 378661 non-null float64\n",
"state 378661 non-null object\n",
"backers 378661 non-null int64\n",
"country 378661 non-null object\n",
"usd pledged 374864 non-null float64\n",
"usd_pledged_real 378661 non-null float64\n",
"usd_goal_real 378661 non-null float64\n",
"dtypes: float64(5), int64(2), object(8)\n",
"memory usage: 43.3+ MB\n"
]
}
],
"source": [
"ks_data.info()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ID 0\n",
"name 4\n",
"category 0\n",
"main_category 0\n",
"currency 0\n",
"deadline 0\n",
"goal 0\n",
"launched 0\n",
"pledged 0\n",
"state 0\n",
"backers 0\n",
"country 0\n",
"usd pledged 3797\n",
"usd_pledged_real 0\n",
"usd_goal_real 0\n",
"dtype: int64"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#check for missing values\n",
"missing_values_count = ks_data.isnull().sum()\n",
"missing_values_count\n"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Percent of missing data = 0.06692001552840139\n"
]
}
],
"source": [
"# how many total missing values do we have?\n",
"total_cells = np.product(ks_data.shape)\n",
"total_missing = missing_values_count.sum()\n",
"\n",
"# percent of data that is missing\n",
"missing_percent = (total_missing/total_cells) * 100\n",
"\n",
"print('Percent of missing data = {}'.format(missing_percent))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Comment: \n",
"We can see that the amount of missing values is very little. However, I always check all the string string value columns for 'weird' characters to clean the data. (sometimes, they contain different values to mean unkown or unavailable without necessarily leaving it empty or with NA) \n",
"\n",
"*Keeping track of all the weirdness here:*\n",
"\n",
"**name:** \n",
"Canceled (Canceled), N/A (Canceled), Cancelled (Canceled), a (Canceled), Project (Canceled)\n",
"\n",
"**state:**\n",
"canceled, suspended, undefined, live\n",
"\n",
"**country:**\n",
"N,0\"\n",
"\n",
"\n",
"Now that we see that there are many different forms and characters, I'd like to delete them so that they do not mess up the rest of the analysis. However, I'd like to see the distribution and total number of campaigns before I remove them. \n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1. What percentage of campaign succeed or fail?"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Success_Percent in %: \n",
"failed 52.22\n",
"successful 35.38\n",
"canceled 10.24\n",
"undefined 0.94\n",
"live 0.74\n",
"suspended 0.49\n",
"Name: state, dtype: float64\n"
]
},
{
"data": {
"application/vnd.plotly.v1+json": {
"data": [
{
"labels": [
"failed",
"successful",
"canceled",
"undefined",
"live",
"suspended"
],
"marker": {
"colors": [
"red"
]
},
"type": "pie",
"uid": "efa484f6-1455-11e9-8f60-00dbdf806640",
"values": [
52.22,
35.38,
10.24,
0.94,
0.74,
0.49
]
}
],
"layout": {
"legend": {
"orientation": "h"
},
"title": "Distribution of Success Status on Kickstarter campaigns"
}
},
"text/html": [
"<div id=\"e3e49635-d86b-4ca0-bee4-cebb6c54b982\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"e3e49635-d86b-4ca0-bee4-cebb6c54b982\", [{\"labels\": [\"failed\", \"successful\", \"canceled\", \"undefined\", \"live\", \"suspended\"], \"marker\": {\"colors\": [\"red\"]}, \"values\": [52.22, 35.38, 10.24, 0.94, 0.74, 0.49], \"type\": \"pie\", \"uid\": \"efa484f6-1455-11e9-8f60-00dbdf806640\"}], {\"legend\": {\"orientation\": \"h\"}, \"title\": \"Distribution of Success Status on Kickstarter campaigns\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
],
"text/vnd.plotly.v1+html": [
"<div id=\"e3e49635-d86b-4ca0-bee4-cebb6c54b982\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"e3e49635-d86b-4ca0-bee4-cebb6c54b982\", [{\"labels\": [\"failed\", \"successful\", \"canceled\", \"undefined\", \"live\", \"suspended\"], \"marker\": {\"colors\": [\"red\"]}, \"values\": [52.22, 35.38, 10.24, 0.94, 0.74, 0.49], \"type\": \"pie\", \"uid\": \"efa484f6-1455-11e9-8f60-00dbdf806640\"}], {\"legend\": {\"orientation\": \"h\"}, \"title\": \"Distribution of Success Status on Kickstarter campaigns\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#before I delete the different states, I want to see the distribution\n",
"success_percent = round(ks_data[\"state\"].value_counts() / len(ks_data[\"state\"]) * 100,2)\n",
"\n",
"print(\"Success_Percent in %: \")\n",
"print(success_percent)\n",
"\n",
"state = round(ks_data[\"state\"].value_counts() / len(ks_data[\"state\"]) * 100,2)\n",
"\n",
"labels = list(state.index)\n",
"values = list(state.values)\n",
"\n",
"trace_0 = go.Pie(labels=labels, values=values, marker=dict(colors=['red']))\n",
"\n",
"layout = go.Layout(title='Distribution of Success Status on Kickstarter campaigns', legend=dict(orientation=\"h\"));\n",
"\n",
"fig = go.Figure(data=[trace_0], layout=layout)\n",
"iplot(fig)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Comment\n",
"It's fascinating that more campaign fail than succeed and by a relatively big margin. \n",
"For this analysis, I will focus on success and failure and will chose to remove canceled, suspended, live and undefined campaigns.\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2. How many campaign per year have there been on Kickstarter since 2009?"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"launched_year\n",
"1970 7\n",
"2009 1329\n",
"2010 10519\n",
"2011 26237\n",
"2012 41165\n",
"2013 44851\n",
"2014 67745\n",
"2015 77300\n",
"2016 57184\n",
"2017 52200\n",
"2018 124\n",
"Name: ID, dtype: int64"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Create new column to extract the year\n",
"ks_data['launched_year'] = pd.to_datetime(ks_data['launched']).dt.year\n",
"\n",
"#count total number of campaign per year\n",
"total_campaigns = ks_data['ID'].groupby(ks_data['launched_year']).count()\n",
"total_campaigns"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"# we have a couple random ones from apparently the year 1970, I will delete those rows:\n",
"ks_data = ks_data[ks_data.launched_year != 1970]"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
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"<div id=\"688d2c8b-6006-4f8f-9763-7e10d9f45b1d\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"688d2c8b-6006-4f8f-9763-7e10d9f45b1d\", [{\"marker\": {\"color\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], \"colorscale\": \"Viridis\", \"reversescale\": true}, \"x\": [2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017], \"y\": [7, 1329, 10519, 26237, 41165, 44851, 67745, 77300, 57184, 52200, 124], \"type\": \"bar\", \"uid\": \"f1d8623a-1455-11e9-9069-00dbdf806640\"}], {\"showlegend\": false, \"title\": \"Total number of Campaigns on Kickstarter per Year\", \"xaxis\": {\"title\": \"Year\", \"titlefont\": {\"color\": \"#7f7f7f\", \"family\": \"Ariel\", \"size\": 14}}, \"yaxis\": {\"title\": \"Total number of campaigns in Thousands\", \"titlefont\": {\"color\": \"#7f7f7f\", \"family\": \"Ariel\", \"size\": 14}}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
]
},
"metadata": {},
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],
"source": [
"#Plot the number of campaign per year\n",
"data = [go.Bar(\n",
" x= [ 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017],\n",
" y=total_campaigns,\n",
" marker=dict(\n",
" color=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n",
" 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39],\n",
" colorscale='Viridis',\n",
" reversescale = True))]\n",
"\n",
"layout = go.Layout(\n",
" dict(title= 'Total number of Campaigns on Kickstarter per Year', \n",
" showlegend=False),\n",
" xaxis=dict(\n",
" title='Year',\n",
" titlefont=dict(\n",
" family='Ariel',\n",
" size=14,\n",
" color='#7f7f7f'\n",
" )\n",
" ),\n",
" yaxis=dict(\n",
" title='Total number of campaigns in Thousands',\n",
" titlefont=dict(\n",
" \n",
" family='Ariel',\n",
" size=14,\n",
" color='#7f7f7f'\n",
" \n",
" )\n",
" )\n",
")\n",
"fig = dict(data=data, layout=layout)\n",
"\n",
"py.iplot(fig, filename='basic-bar')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Comment\n",
"Wow that is a lot of campaigns, Now I want to take a deeper look at how many campaign succeed and how many fail.\n",
"\n",
"But before, we have to do some more data cleanup. including removing the 'weird' characters and adjusting the time information."
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"# drop rows with'weird characters' in it\n",
"#name: Canceled (Canceled), N/A (Canceled),\n",
"#Cancelled (Canceled), a (Canceled), Project (Canceled)\n",
"\n",
"#state: canceled, suspended, undefined, live\n",
"\n",
"#country: N,0\"\n",
"ks_data = ks_data[ks_data.name != 'Canceled (Canceled)']\n",
"ks_data = ks_data[ks_data.name != 'N/A (Canceled)']\n",
"ks_data = ks_data[ks_data.name != 'Cancelled (Canceled)']\n",
"ks_data = ks_data[ks_data.name != 'a (Canceled)']\n",
"ks_data = ks_data[ks_data.name != 'Project (Canceled)']\n",
"\n",
"#State\n",
"ks_data = ks_data[ks_data.state != 'canceled']\n",
"ks_data = ks_data[ks_data.state != 'suspended']\n",
"ks_data = ks_data[ks_data.state != 'undefined']\n",
"ks_data = ks_data[ks_data.state != 'live']\n",
"\n",
"#country\n",
"ks_data = ks_data[ks_data.country != 'N,0\"']\n",
"\n",
"#drop na\n",
"ks_data.dropna(axis=0, how='all',inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of columns: 16\n",
"Number of rows: 331465\n"
]
}
],
"source": [
"#check the shape of the data set\n",
"num_rows, num_cols = ks_data.shape\n",
"print('Number of columns: {}'.format(num_cols))\n",
"print('Number of rows: {}'.format(num_rows))"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"#Now it's time to transform the date data to date format. some include the time, so it needs to be fixed\n",
"\n",
"ks_data['deadline_date'] = pd.to_datetime(ks_data['deadline']).dt.date\n",
"ks_data['deadline_month'] = pd.to_datetime(ks_data['deadline']).dt.month\n",
"\n",
"ks_data['launched_date'] = pd.to_datetime(ks_data['launched']).dt.date\n",
"ks_data['launched_time'] = pd.to_datetime(ks_data['launched']).dt.time\n",
"ks_data['launched_month'] = pd.to_datetime(ks_data['launched_date']).dt.month\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Comment\n",
"This information is from the data read me files:\n",
"\n",
"You'll find most useful data for project analysis. Columns are self explanatory except:\n",
"usd_pledged: conversion in US dollars of the pledged column (conversion done by kickstarter).\n",
"usd pledge real: conversion in US dollars of the pledged column (conversion from Fixer.io API).\n",
"usd goal real: conversion in US dollars of the goal column (conversion from Fixer.io API).\n",
"\n",
"this tells us that the data is mostly duplicated, I will proceed using the us_pledged_real and usd_goal_real to reflect the accurate amount in US dollar and to keep the scale consistent."
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"#drop duplicate columns including the ones with empty values \n",
"col_drop = ['usd pledged', 'goal', 'pledged',\n",
" 'deadline', 'launched']\n",
"for col in col_drop:\n",
" ks_data.drop(col, axis=1, inplace=True)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['ID', 'name', 'category', 'main_category', 'currency', 'state',\n",
" 'backers', 'country', 'usd_pledged_real', 'usd_goal_real',\n",
" 'launched_year', 'deadline_date', 'deadline_month', 'launched_date',\n",
" 'launched_time', 'launched_month'],\n",
" dtype='object')"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ks_data.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### let's dive into the campaigns and look at their success or lack thereof. \n",
"# 3. How many campaigns in each category have there been on Kickstarter since 2009 per year ? and how many were successful?"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Main Successful Categories :\n",
"Music 24105\n",
"Film & Video 23612\n",
"Games 12518\n",
"Publishing 12300\n",
"Art 11510\n",
"Design 10549\n",
"Theater 6534\n",
"Technology 6433\n",
"Food 6085\n",
"Comics 5842\n",
"Fashion 5593\n",
"Photography 3305\n",
"Dance 2338\n",
"Crafts 2115\n",
"Journalism 1012\n",
"Name: main_category, dtype: int64\n",
"Main Not-So- Successful Categories :\n",
"Film & Video 32892\n",
"Publishing 23113\n",
"Music 21696\n",
"Technology 20613\n",
"Games 16003\n",
"Food 15969\n",
"Design 14814\n",
"Fashion 14181\n",
"Art 14131\n",
"Photography 6384\n",
"Crafts 5703\n",
"Comics 4036\n",
"Theater 3708\n",
"Journalism 3136\n",
"Dance 1235\n",
"Name: main_category, dtype: int64\n"
]
}
],
"source": [
"main_catigs = ks_data[\"main_category\"].value_counts()\n",
"#separate the data into failed and successful campaigns\n",
"main_catigs_failed = ks_data[ks_data[\"state\"] == \"failed\"][\"main_category\"].value_counts()\n",
"main_catigs_success = ks_data[ks_data[\"state\"] == \"successful\"][\"main_category\"].value_counts()\n",
"\n",
"print('Main Successful Categories :')\n",
"print (main_catigs_success)\n",
"\n",
"print('Main Not-So- Successful Categories :')\n",
"print (main_catigs_failed)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
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],
"source": [
"#number of campaigns per category plot\n",
"trace_0 = go.Bar(\n",
" x=main_catigs.index,\n",
" y=main_catigs.values,\n",
" name=\"Number of Campaigns per Category\",\n",
" marker=dict(\n",
" color=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n",
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" \n",
" colorscale='Viridis')\n",
" \n",
")\n",
"\n",
"\n",
"data = [trace_0]\n",
"layout = go.Layout(\n",
" barmode='group'\n",
")\n",
"\n",
"fig = go.Figure(data=data, layout=layout)\n",
"fig['layout'].update(showlegend=False, \n",
" title=\"Total number of campaigns on Kickstarter per category since 2009\",\n",
" bargap=0.05)\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"#fig = go.Figure(data=data)\n",
"iplot(fig, filename='basic-bar')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How many campaigns were Successful throughout the years (per category)? "
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
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12518,
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11510,
10549,
6534,
6433,
6085,
5842,
5593,
3305,
2338,
2115,
1012
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Successful campaign vs. failed campaigns\n",
"#First plot\n",
"trace_0 = go.Bar(\n",
" x=main_catigs_failed.index,\n",
" y=main_catigs_failed.values,\n",
" name=\"Failed Categories\",\n",
" marker = dict(\n",
" color = '#fde725')\n",
" \n",
")\n",
"#Second plot\n",
"trace_1 = go.Bar(\n",
" x=main_catigs_success.index,\n",
" y=main_catigs_success.values,\n",
" name=\"Success Categories\",\n",
" marker=dict(\n",
" color= '#424086')\n",
" \n",
")\n",
"\n",
"\n",
"\n",
"data = [trace_0, trace_1]\n",
"layout = go.Layout(\n",
" barmode='group'\n",
" \n",
" \n",
")\n",
"\n",
"fig = go.Figure(data=data, layout=layout)\n",
"\n",
"\n",
"fig['layout'].update(showlegend=True, title=\"Number of Successful and Failed Campaigns on Kickstarter per Category\",\n",
" bargap=0.05)\n",
"\n",
"iplot(fig, filename='grouped-bar')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 4. How many total backers per category since 2009? (credence to the crowd-sourcing claim)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of Backers in Successful Campaigns :\n",
"main_category\n",
"Art 1040022\n",
"Comics 1351864\n",
"Crafts 199124\n",
"Dance 148220\n",
"Design 6485969\n",
"Fashion 1213688\n",
"Film & Video 3603935\n",
"Food 1098695\n",
"Games 10194460\n",
"Journalism 154134\n",
"Music 2472579\n",
"Photography 359407\n",
"Publishing 1953524\n",
"Technology 4617545\n",
"Theater 460684\n",
"Name: backers, dtype: int64\n",
"Number of Backers in Not-So- Successful Campaigns :\n",
"main_category\n",
"Art 124655\n",
"Comics 86455\n",
"Crafts 34252\n",
"Dance 11816\n",
"Design 467361\n",
"Fashion 139896\n",
"Film & Video 497340\n",
"Food 201480\n",
"Games 703519\n",
"Journalism 24513\n",
"Music 199980\n",
"Photography 59448\n",
"Publishing 235766\n",
"Technology 417730\n",
"Theater 42800\n",
"Name: backers, dtype: int64\n"
]
}
],
"source": [
"num_backers = ks_data.groupby(['main_category']).backers.sum()\n",
"\n",
"num_backers_failed = ks_data[ks_data[\"state\"] == \"failed\"].groupby(['main_category']).backers.sum()\n",
"num_backers_success = ks_data[ks_data[\"state\"] == \"successful\"].groupby(['main_category']).backers.sum()\n",
"print('Number of Backers in Successful Campaigns :')\n",
"print (num_backers_success)\n",
"\n",
"print('Number of Backers in Not-So- Successful Campaigns :')\n",
"print (num_backers_failed)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#First plot\n",
"trace_0 = go.Bar(\n",
" x=num_backers_failed.index,\n",
" y=num_backers_failed.values,\n",
" name=\"Number of Backers for Failed Categories\",\n",
" marker=dict(\n",
" color= '#fde725')\n",
")\n",
"#Second plot\n",
"trace_1 = go.Bar(\n",
" x=num_backers_success.index,\n",
" y=num_backers_success.values,\n",
" name=\"Number of Backers for Success Categories\",\n",
" marker=dict(\n",
" color= '#440154')\n",
")\n",
"\n",
"\n",
"\n",
"data = [trace_0, trace_1]\n",
"layout = go.Layout(\n",
" barmode='group'\n",
")\n",
"\n",
"fig = go.Figure(data=data, layout=layout)\n",
"\n",
"\n",
"fig['layout'].update(showlegend=True, \n",
" title=\"Number of backers for Successful and Failed Campaigns on Kickstarter per Category since 2009\",\n",
" bargap=0.05)\n",
"\n",
"iplot(fig, filename='grouped-bar')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 5. What is the average run time per campaign? How quickly do you know if your campaign will succeed or fail?"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"#create new columns to calculate how many days does an average campaign run\n",
"ks_data['run_time'] = ks_data['deadline_date'] - ks_data['launched_date']\n",
"ks_data['run_time']= ks_data['run_time'].dt.days"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
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"data": {
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],
"source": [
"ks_data['run_time'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Average Number of Days in Successful Campaigns :\n",
"main_category\n",
"Art 30.340052\n",
"Comics 31.908593\n",
"Crafts 29.026950\n",
"Dance 31.814799\n",
"Design 33.468101\n",
"Fashion 31.309136\n",
"Film & Video 32.310308\n",
"Food 31.511586\n",
"Games 29.912766\n",
"Journalism 32.036561\n",
"Music 33.885999\n",
"Photography 32.137368\n",
"Publishing 32.012195\n",
"Technology 34.160112\n",
"Theater 31.615702\n",
"Name: run_time, dtype: float64\n",
"Average Number of Days in Not-So- Successful Campaigns :\n",
"main_category\n",
"Art 33.955629\n",
"Comics 36.651883\n",
"Crafts 32.345082\n",
"Dance 34.703644\n",
"Design 34.616579\n",
"Fashion 33.127142\n",
"Film & Video 36.947768\n",
"Food 34.804621\n",
"Games 34.021683\n",
"Journalism 34.964923\n",
"Music 36.890717\n",
"Photography 34.228070\n",
"Publishing 34.641025\n",
"Technology 35.607675\n",
"Theater 36.304207\n",
"Name: run_time, dtype: float64\n"
]
}
],
"source": [
"num_days = ks_data.groupby(['main_category']).run_time.mean()\n",
"\n",
"num_days_failed = ks_data[ks_data[\"state\"] == \"failed\"].groupby(['main_category']).run_time.mean()\n",
"num_days_success = ks_data[ks_data[\"state\"] == \"successful\"].groupby(['main_category']).run_time.mean()\n",
"print('Average Number of Days in Successful Campaigns :')\n",
"print (num_days_success)\n",
"\n",
"print('Average Number of Days in Not-So- Successful Campaigns :')\n",
"print (num_days_failed)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
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"<div id=\"03fff016-ebbd-4cb0-8f2b-b40697914ff9\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"03fff016-ebbd-4cb0-8f2b-b40697914ff9\", [{\"marker\": {\"color\": \"#d8e219\"}, \"name\": \"Number of months for Failed Categories\", \"x\": [\"Art\", \"Comics\", \"Crafts\", \"Dance\", \"Design\", \"Fashion\", \"Film & Video\", \"Food\", \"Games\", \"Journalism\", \"Music\", \"Photography\", \"Publishing\", \"Technology\", \"Theater\"], \"y\": [33.95562946712901, 36.65188305252725, 32.34508153603367, 34.703643724696356, 34.61657891184015, 33.12714195049715, 36.9477684543354, 34.80462145406725, 34.021683434356056, 34.964923469387756, 36.89071718289085, 34.228070175438596, 34.64102453164885, 35.60767476835007, 36.3042071197411], \"type\": \"scatter\", \"uid\": \"35734452-1456-11e9-bbee-00dbdf806640\"}, {\"marker\": {\"color\": \"#424086\"}, \"name\": \"Number of months for Success Categories\", \"x\": [\"Art\", \"Comics\", \"Crafts\", \"Dance\", \"Design\", \"Fashion\", \"Film & Video\", \"Food\", \"Games\", \"Journalism\", \"Music\", \"Photography\", \"Publishing\", \"Technology\", \"Theater\"], \"y\": [30.34005212858384, 31.90859294762068, 29.02695035460993, 31.814798973481608, 33.46810124182387, 31.309136420525657, 32.310308317804505, 31.511585866885785, 29.912765617510786, 32.03656126482213, 33.88599875544493, 32.13736762481089, 32.01219512195122, 34.160111922897556, 31.615702479338843], \"type\": \"scatter\", \"uid\": \"357357b6-1456-11e9-8573-00dbdf806640\"}], {\"showlegend\": true, \"title\": \"Average number of days for Successful and Failed Campaigns on Kickstarter per Category\", \"bargap\": 0.05}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#First plot\n",
"trace_0 = go.Scatter(\n",
" x=num_days_failed.index,\n",
" y=num_days_failed.values,\n",
" name=\"Number of months for Failed Categories\",\n",
" marker=dict(\n",
" color= '#d8e219')\n",
")\n",
"#Second plot\n",
"trace_1 = go.Scatter(\n",
" x=num_days_success.index,\n",
" y=num_days_success.values,\n",
" name=\"Number of months for Success Categories\",\n",
" marker=dict(\n",
" color= '#424086')\n",
")\n",
"\n",
"\n",
"\n",
"data = [trace_0, trace_1]\n",
"\n",
"\n",
"\n",
"fig = go.Figure(data=data)\n",
"\n",
"\n",
"fig['layout'].update(showlegend=True, title=\"Average number of days for Successful and Failed Campaigns on Kickstarter per Category\",\n",
" bargap=0.05)\n",
"\n",
"iplot(fig, filename= 'basic-line') "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Comment\n",
"This information is very interesting, we can see that most campaign either succeed or fail within a month. This means that anyone who has a campaign must be focused particularly within the first 30 days as those seem to be the most crucial.\n",
"let's get a different perspective to confirm these findings."
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [],
"source": [
"# calculate how many months each campaign runs on average I need to \n",
"# Conversting the days into months was producing negative numbers, so i went with this solution to speed up the process\n",
"ks_data['launched_date'] = pd.to_datetime(ks_data['launched_date'])\n",
"ks_data['launched_date_month'] = ks_data['launched_date'].dt.to_period(\"M\")\n",
"\n",
"\n",
"ks_data['deadline_date'] = pd.to_datetime(ks_data['deadline_date'])\n",
"ks_data['deadline_date_month'] = ks_data['deadline_date'].dt.to_period(\"M\")\n",
"\n",
"#Creating a new columns with Campaign total months\n",
"ks_data['run_time_months'] = ks_data['deadline_date_month'] - ks_data['launched_date_month']\n",
"ks_data['run_time_months'] = ks_data['run_time_months'].astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"data": {
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7tW4qcaC7qHBfIw5a2f2l6FXugnk68aqFwjRrEcFYLs2vSuT1KWCdMstiTeD6TD57lEhXdh8hDvS5Y81Sily9Jg7stxX8tvEl5je7IN3XiqRZnTgJ59J8oUiav2S2/13LLIfVS62POm2T2W3pkjRst8ywFe4uEK98cuDO9L3inU/iTkZ2H+1dJM1IIhB3okXJ9hXK68QFlK2LpNuRfKuUNyi4+1kkr7OrWFZnF8z7TmDNIum+k0lzazu32/8qNz6TbkvyV5Pv6MB2kP1tJZcFEfAvyaQdXiJdo+58evq9xxVJ0wt4uNy228F9pRl3Pp1ovVN4jF6T6IQxl2YKcbFujyL57ZlJ93iJee5EviXIDOCjJdIdmFnf93dg2X2l4De+WGyexLHnbfLH62FF0tyaGb/CeS+T7quZ+X23neXeo6DcU9O+u3E78qrbnc+0XT2ZSTMe6FUir57AAUWGH5GZ/jVg9zLl+RgFdUCinp07FywBvlxi2o3J3xl9H/h4wfjDMuUoWl/IpN2Fgjt7RM/DuTLsVGba1YEd2rkd9Gb5mOUl4LTC31JDfrk7oO/XsO2UbBVKBOj3Z5Zx0f2C5Y81fSvMs27H8Easo1oWdnYHfjvz+eftWHGjM9OPLjI+u1PdRw2BbSaPszN5DE/DeqSdPDd8EuWbBOaaMvy5nRto2d/Z3j/iypIDs0qMH16wvg6sIs9JufRVpD04k/dZFdIOIH+SXKG5Q8F6ehvYtA7LJ1vJKNqsrJF/7Vg/O5dItxrxHqcPKiJAjxJps/tM0XVSMM9rypR/I/JNN9+hTJBZYTmsnTlWlKqUVjoWZIP5kuuSuHL7Ribt+BLpZmfSXF4mv73LpSNaAjjwWLO3rzLb0iWZ4c+mYbcUpB+aSX9EGlY2+CSuaOeOhQsoU3ErWF9XV7Htf6pMXleXS0fHgs/XgD4l0q1CBMWefvcK+1wV2+265APxv5UpU7YZ4iEd2A6yv+3sgnE9iJYZJxFXynPpHiyTXyODz9+UyWtUNenauYyaEXw+CqxaIt2XCrb9/yoz33sy6TYpMj4XwL0LbFnhN5yfyatdF8JYMfjcq0zakzLpfldk/IHV7LdEayonWgVt2IH1flFB2XN/c4g7r6cRQXPZeib1DT6zj/BMqjTvItOvQjwaU3F9lMnjC5npz6iQdiD5RxQuKhh3ZiafQe0ox6w07dT2ruMq53MYxR+zeINohXA+sD8lbnQV5FVT8Fll+QZkylT00RVaF3zWfR21t8OhNdP/hURz0nrLPlR7ppfplapaFh2o3ES+F8ibgVFevElhTq6p1NZWxcPxTZTrIOSjVtCBQREvEM0U6+lL6f9iKqx/d3+WuD0PcWeknBvd/aUOlg3ibllOpWavjVDL+pnq7g8WG+Hu75HvmRKiMvZ+iXz+lvm8TYk0WSVfw+Hxfshc5w69iAphzdx9IXG1H+JKfXsclPn8v2Xm9QbwuxrzLpkf0cQ0t6yLLc/csaGvRc/KnU2uB9v9zGzDzPBj0v+3iONhNfYnLoQAXOnlezG/iDgvABxUrpMLYJq731dmfLa5azXbdC2ucvf5xUak802uid9qRDP6mqTtMfceyd3MbIXyW3SmNDp9fZVo7lYPy3W+RFytnk0cq3PN656m/DsxG6ncfncvcWcU6r/Om+ESL92s8/7M5yXE84yllDyep3NK7pj8J1/xcaFC2Y56Kp2Dq/GYu99dZvxl5F9VcpCt2HHSX4mLEQDHFhmPmQ0mWj9APDP/ansL6+7fIDogLMxjc2If+DGxvF80s1PNbDUaL1vH/X476rg7EQEDxEWRcuujlFw9bhFxt74kd3+KuGsMK25D2UcKKnZMVERu+s3T4x0N4e7XEhcFpheM6pOGn0G0aHrForPMsh04NqB8zxIXRaH99aVGqfs6am/w+UT6vzYw0cw+Uo/CZOSeWVtAVAI7qg8wgWg7D/FczSHuvqjCdHel/wOBCRa9d5btBbQeLHpKu8zMHrPoAe/9gspE9jmKzSpk9zdPly7q6FPp/6vAcIveiEv+EVebAD5SYfmVq4h2GnVeP/+oMP6VzOeHSqZaPl2fkqnCAvInklKyFf8diiUws9XM7EsWPZ/meqJeVrAsdk7JK/YcWyT/Vci/vunfXvl5pUk1ZP8OK56EPuDxnGTuRFBseeaODesC95rZ4Wb24Rrm32hXEZX4HqSKTrqAdngaf527V/sOsx0zn+8sl9Dj2cZcxXktygcQRS+6ZGSfyam0TdeqGfP+TebzV4qM3598j9zjvTnPNzrxnO1Qd3+5CfMr9Ja7Fz4P9YG0TeZ6yq/3Om+Gcsfz7DH6qXRxrpq0hcvhk+Sf31tcxfk32+P8wIq/oLKygU6qV+VeVbI2Be/MTsF5rkfNzSl+cfOrmc/lgvSquPvFxN3/Q4hn1XMtV7I2BS4A/lbFReN2MzMj31vq6+5+f7n0JWT7dai5T4tUhlwerwJ7VbEd5Y5PHy24GXNX5vOlZnZW6hOkWrnpNyDOpV9sVBDq7hOB7YHdiQvwDxE3UbLWIp6jfczMdqROzKy3RQ/uf7HoUfhtK+itlnwfKjXXlxqs7uuovR0O7UVUTrclduhJZraHl3mHYrXSD8pV4p6uU+D0W6LNN8CP3f2MKqc7jdhBNyU21t2B98xsCnEV8x7gnnpVGtIdlOup7epkpQpvex6qLik9XJ67s9iX6u+e5PSh9It761XW7EPY69Qpz0atn8IHxgtlO1oqmdbd37N8D+ZlX/UC/LOK/WpW5vMKrz6weIXOjRTpQKCE9gRmvYnnWSF6haykmjQ5r1exDHLLvtjy/DFxMWsb4mT2B2CpmT1KHBsmEs/wteQl1e7+opndQ1zRHU10WPAZ8ie38TVkt0nm87NVpH+W6OwkN22pIP+1EsNzstt+pW26Vg2ft7vfb2ZPEB0jfcnMTvflO3/KBqRlu8KvUbbzJSOa0e9AXIRYjWgafQ/R8UWzVTreQfn9rrMr9/uqOpYXSVu4HPplPo8mf/e8GvUI6GdVTrLC+eOpgvGXAWcRzx1+hWhGDIDFq8pydwZnEzcOOiwFxTemP9LFwjaiw8gvEXVZ0rDfA0VfGVMH65BvPdjeDqCywUnhsq1Gb/J1oy1oXz3uVQB3n25mPyEuaq0NjAXGmtkcohXYZOCvZeKD84j3DX+ceCzkWuD9IufSSjeLqpLO+/elP8zsQ8Bg4qbKF8nfddwQuMXMPu6VO44sy8z2JlogbFTlJJ3pQjY0YB21685nagKxJ/mNvh8RgG7envwKZBf623XID5YPsqt+55W7zyYW9IXkm2+uRly1OpV4CHeumX3brPz7DKt0A/nAZiFRoT2VODB+nmgi8jmicpFT6Z1w9a78drSJ4YfKjKtXWbNNd2tuMldGI9ZPLc1tOtz8PCnX82bOfzKfl9tnzGxdokKQCzxfJDqk+Rbx7OnB5JfFkylNe441a2Y+11rmSjq0LFOTzZ2Jg3LuLkWud8CTiZP5KxbvUS23zTfS+PR/sJkNJfNuT3ev+t2eRIUip5plnD1ul7s6Wq/tuT2aNe/c3c/1yTRzNbPNyAfok9x9Zh3n+bS735z+bnL3S9z9OKKzodeICtAd1pr3abZynTdDtb+vI8uhI+fgehyLOnT+APDoafQv6etnCpo4HkI+SL6sAS23cmV4y93vcfeziHPZxZnRI81s5xKTdlQ96rgdzaOu9Th3P5VYbw9nBm9BPGd5ETA73fHbigLpEYWdiFdA5ZpG9yAuAnyLeETuFYt3wtf98Td3X+zuU9z9F+6+M/lnsyGOlV8vPXVlZvYx4tG3XOD5FPFmg28QLZE+l/nLXZRq9Luea9KIddTeZre5AHQE+Ss3/YkmuB29XZx9BrNeL0c+nvwV+5PMrOrnVN39VXf/JnEVZFfiqvHNmXJuSNxV+E3xHKpjZrsTdykgehb7qLsf6e4/cfer3f1PuQoFUdlvleyBbpK7W41/s5tQxmwzlh2tDi9tX4nWTzXWqJxkucCv8OR2ErHdQzxbuKW7f8Pdx7n7NanCm1sWHbmgkK3A1FrmhnP3he5+JnFlfxjxWqXryN9VW5u4un9LnS5O1epP5I9Tp5IPdq4snrykbPPAapZx9rhdrmlhd3AV+e0425TwWPIVjA43K6xGarZ+XPq6fh3n2+56hLRL9nh8VI3n371L5lq9jp4/ci5J/7PPPkN+P3mfaLXWcKn12sksfydyrw5kWW6fqEcdt6N5ZNfJhHbU4+YWZujuN7r7jsRd2cOJZ8xzrV6MeMzgYYvXuxRO+5a7f49oKZO7gHs9+XPph4lXm93cjt9aE3e/mmiandOR7QDiFSW51gvnuPs27v4dd7/Y3a/NXCi8mdYFnRWP4fVeRx06aXh0TLIn8b5DiLtME9NV3fbmuZD8w+ofq1Ol7SWiZ8RcOWsKQFO5lrj7A+7+M3f/HNH2+VjyzWO+mpoitlf2pPB9d59XJm29n7GtmrsvIH/g2qZFlepKHifeHQdxhe/AOuS5UqyfKn20ivWWvUJZ2AlUblm8D3zbS3eCBB1bFgvIX2Xfsor01aSpO3df5u7T3P1Cdz+MuML5OfLPru1LnHibXa53iZMDxBXoHtTwbs+M7LOB1TSzzqapRwdiKy2PDu1yTWD3NLMt0753bBr2BqkZYJPKcwv557k/nZqDFZNt9lnpblnDno+TorKPp7Sng5eOWuHuVYU0pY4BdxDvBQc4zsIA4vEmiKaaTTt+pPNYto+RwpYB9don3iRfh/p4daVbQTb4a89zvPPJn1vrug25+79SUHWyuw8mfuPENHodolluqWmXufsj7v4rd/8i0RP7IeRbHn7azNrVAWKNss81d7SFSO4Y+zJwTqlEZrYOHb8jndWQY3i91lGHr1h6dFqwJ/k7i1sRAWhHVliuw4p1yB+IOiSVcwTLB6Ble/iqkN9id/8ty/f2ultBsmzTmkqV/Wxb8H+WSpSa8A2vpow1+qCsVQQmuQN07m5wp5Ka6WTXy/ctepbsiFavn3rqTb4jn1JGZD4/XDAutyxe9zLPQqSmnhuUGl+JRw+AuefSNrZ4KXc5w9s7r3pKB+ebgR9kBn+yVPoGG1/w/e5iV60ryHZ0VfYl0hYdiuV+69u073mkatVyfG2lXKsYI+487kP+ub2rPHq1bqZsBei8Emmy+3Wlc3ln65mxq7s38/mzLbgAvGe5kemZzV3S14Xk61zLSefp3N33rYjjd/Y56Ka0CCiQfSa78I5tXfaJ9LtzrbPWM7PCemM1sp0z1nxxPZ1bc/XsTcysYfuwuz9DBCe543XV50J3X+ruNxKvksppxrm03HYA+d9Szb6Xqy89l9Z9KftUkV8t57ymHMPbu47q0lwmXZ0aQf4h862Be8xsk9JTlZXtGvxcK9IVd3ukAHQ4+aYVJ3YkAE1mZz4XBjjZjbZSc7XscxTlnlM8gQ5U6MuopazZZnvn16NZawP8mvwdm08Av6j2JG1mvcyssGliq9dPvf1XqRFmtgFwVPr6LvFsc1ZuWWxYocezH5QZV63s6ye+VSpReg71qFLjW2R25nNHL360i0dPiv9H9ML5Dyp0qV/CX8lfRT3aln91S6ETyD+PdLOXfu1EPdRyzGoZd3+YfO/Sx7D8M0T17Gio2vJMJv86qB3N7NNF0iwkf3dlJzMr2tQyXWzr0DNRUptUj8n2xD+6yUUYYmYjyow/lvwdnJu9/GtELiffi+o3yL8Kby4rnndqZmbVdvCS6w08e8emsFfmZ8mXdUSp+kSV56JsHfe8dtRxHyLe8wmwt5m1p2loto7zo3rVs4vxeGYw11S4PefC2ZnPNU9vZuvX+PuyAX2x3rlz555V0sWWcnL1pZL1xnRzpJqOUKs+57XgGD4787niOqrbxpYJQHN3hT5GBKDteVfOH4mmkxAR9FVlFlwPM6u6SZvHO+qyz6qeaGa/LpLvJmb2UzPrXyqvVKajM4MeK0jyfOZzpTtN2btLP7Ai75oys88QvWw2Qi1lvYF8eXcHfl8uCDGz1c3saDM7rINlrFpqHvwF8u9qPAm43sy2KDVNavZzKDCNeAl0VqvXT70dYWYrHHDMbE3gGvKVh8uL3N3MLQsjXtxemIeZ2Vjgs3Uo53jyV/CON7OuJTnLAAAgAElEQVQjisyvF9E74bqF4xrFzC41s+3KjO/B8s/4FR4bmsbdD3T3ndNfzd3ypybmuWdg1iH2oxV640sVoNz28D7w0/aWuUq1HLNaLXf3cxPyHQ/93d2fLJG+0X6U+TymRJpc5X+tYmnSNn4Z8XJ0aa7vkw+ELjKzw8slNrOPmNnPzGy9culqcGWxupGZ7UL+HLiM6FilJI++Q3I9rR5Cvi+BK+p04epCM7vdzPYr1/op1eUuJ99c+C0KXmGSWijk7jpvSZEKu8XbAK6l8rnoWvKd8e0BXGElXkOX6rjLXSBKAX324u51ZvYpSjCzrW3Fdw1fS75l0Qjgd6n8pfLoZWbHmNkXCoZ/28w+V2H5Hk6+d93HMsNXNbNLipQtO209zqWHAU+Y2bEV6qpmZt8k/w5UWP5CQU576vYbp7wL5/kh4vw6tEI+tc4X6nAMb9Q6quvVeHefm66ITSJ2zo8TAehwr+Elwe6+NAUBDxA78ZHE8zLXEj/qHaKN8jDi9QGLiKvz1eb/71TOe4grh99IF7FOytwWXw34b+C/zexhopnDU0RFuDcRXB9B/j2O97H8i6EhHrZ+lTigHmVm84j3y+U6YXnX3XMHs5uIZzk2I96rN8PMLideH7EO0c3xZ9Jv/xPRo2g93U08QAxwuZn9AniB/Ds6Z7n7LIgDn5l9nlg/mxHdU480s+uIK/xvEp0SbE7cddyH2PjPqnOZy3L3v5nZwURgsjZxcvuMxSso7iXujL5HrJ9BRIcspTrMavX6qadHiTJfbPH+rpuIdTaAaBaYe07zeaKSU+gi8h2mnGxmQ4jf/G9inR9BHEhnENv6J9pbUHd/zcy+Q3Q8sQpxoeOLxP6+gGhlcQxxvPkjcGiatNG9an6VeM77SeJ5lieI5/fWTGU5jPyzj88SF2xWZqcRHS8MICpLM8zsCmIdr0E81/JF8hc0x7h7QwNud59vZtOIbW2EmV1CHMcWZtJ0+O5JnfyBCMazQfulLSoLxP4znTju7Whm+7t74Tn0f4mLqz2BU1Pl4yaiA6WtiAt0A4hKbNMuLErcTTezk4hOe1YH/mBmpxAB0z+J89o6RP3mk+Tf1fyTOsz+ZuLC4mNmdhkwhdhGdie2iVy98ifuXs3rfH5DXCjOWUYEgvVgxDP3+wLzzGxSKu/LxLl6XaIX6M+z/KM13/bo0bzQT8k/w3ehRZPZO4gLAdsR56JNqbBPuPv7ZnYI0QKhD7Gf7ZOp475LtKDK1XEXknkdTcrjWjPbgwiC1yPev3h7Ks/LxHN+HyWaSX8yzWNGZvplqX70AHFR7AhgVKrHPUK+HrcF0avp3sT5rfAOXRvR6eYbZnZHmvYlYj1uTCz73FsCnOUv0BvwNeBrFq+lmsSK59LDyV8UeJraXwuTM5DYri40s8lES6AXiHrEmkS8chDLv5v6Sncv9l7bu4k79QC/NbNfAnPI15efdffcq99+Rf4xpnFmtifRcuF14vh5NLGe7iK2oXItRrNl+anFnf2Z5G+yvFhwQbMex/DGrCN3r+qP2Gic1GS9QtotiEp5bpongA0z40dnxo0uk8/WxB1Qr/D3XJFpz86MH14i/42InTGX7teApXEfqWK+ub97gPVKzOP4MtPNLki7c1qhpdLPJ4Kcsr+NaFqcG392let3VSKALjXvFfIhdpIJVS6j94GvtGc9dfSPOOjcWsP6fAQYWSSfpq8f4s5fLm2/KvfRSZXGEyfcuWV+yyyiF9tS8zqBONCWmn4GcTCalBtWIp/RmWnKHQtOSdtQqfn9Ma3n3Pf/LZHPbIrse7WmJU6s1WxLj1Vabx3ctrPb0iXtzGPjSttOSrcRUVEp93uXAGdUWd5K237FtMQFo5LbRUHaszPjVtgva0lb7XZbMM1FmWneBNao43aQLW/Z5ZqZ5ojMNA+VSPO1Ctv6JcR5Ovf9shL55I41s6ooV9Vpa1xG52bKeWaV0/TITDOhRJqrM2n6diSvTNqvZNIeVSbdQcQF7mqORfOAPu1cdsuVB/heFduF1ZD/M5lpb63jOv8hEYhXs3xyy+iICnmeV2b6pcCZRKBWcVsjbmI8UUW5ni0xvRH7/uIq8ij6u4hgeWKVy+d9Co53RAd21Uy7sLAMRL2z2nUzDdiindvBgZSvuxX+LSaC5FXL7Mt/LzP9mQXp/6fC/CYTFxAqHvuITgRL5bPC8ZcOHsMbtY4a0sbb42WyI8i3Ad4WuNviWbJa8plJVJIPI+4czCGuCC0mrqzcRVyRb9cDyB699Y4gfzXoG8RVEXP3F4gg+hhi55pOXCFZSlwxm0k0T/yMu+/p7kVfGu3ulxLPEdxMbFglO5dw9weJF9ZfSFy9XJzm+QRwAbC9u99aavqO8Gjisg9wOlHBnE/+Kk6paV726Lp9OHH18kmiUrWUaLYyg3j1xAnA5u7e9OebUjmfcvdPE3fgxhJB9ovEtvQeccfuPuKq8E7uPszd7yyST8vWT725+6PEHaNzifIvJJ4nmEZULAZ7/spdsekvJjrY+iOx/JYQlaC/E8+Ttnm6U16n8v6UuOP8O2I/Wky8X/Mu4DB3P5Tl7yq9sUIm9bUxcbXv/xEXK3L7y7vEce8mosXGMG/O64UaLh0vdyXucN5E/nj2FrHv/xLYxt1/VDKT+pfpNmI7/ANxp77e7zWupwmZz79392rel9hI1xEXiQF2sCKPr7j7b4h1fgP5/fxl4jniT7v714lKh7SAu/+Z6LzqG8Q6yZ3XFpM/Ho8DDgA29eJ389oz3/OJFhDXpXnm5ncLMMrdv+6p5lql7B2dunU05PEOzw2JVk/jiEr+v4hltJQ4fz9NnMeOI16h9ocKeX6fuOh1K/GaicXEsfBa4FPuvsKjKGXyegYYTFwIupGo4y5Kef4LuBP4LiU60/NwNhHEXkC0PHuDfB3scWJ57ke+1+3CPF5y9xHEHdJLiXpbYT3uWiKI6evu4wuy+CpRj/4hEcT+izgvLCGWz31EE+GtC5dtqndunH7/ZUT9o/Bc+ifiXNuWYouaeTxushHReueHRHPUfxJ1nmXp/wvAbUQdeCt3P91LNP326Bl5L6Ku9CAV6sse70HdP+X/Ovnj6N3EdjeiVAxRxOHAiUTrvdfI3/UsNe8OHcMbtY6stuODiKyszCy3s9/r7sNbWZZGSM9TjEtfD3b39jbPEak7M7uauCABMDRdABLp1iw6LJxD3IH7N3GhumyFWkRWbno5tIis9Cx6Kvxa+rqEfFf2Ii2XWv0ckr4+pMBT5AMHkn8VxBUKPEW6PgWfItKpmdlaZlay06LU8/AV5F+WfZPX0MGZSBOcQXRiB/m78yLdWrrrmeuIcAnx/JmIdHEtefeciEgN1gGmmNnjxDOeM4hnddYinpf5Ivlep+cTvVSLtIyZbUb0JLsG8WzQCWnU05R49kqkOzCzwcSdzvWIPjVyr5i43N1fbFnBRKRpFHyKyMpicPorZQ5woLvPLZNGpBn2IV4PlPUecGypTixEuolTyT/7nPNPVnyFh4h0UQo+RaSze5norW8U0bP1hsR7fo3oOe4xoue28e6+qFWFFCnh38BDxLtP9aynSHifuGB4GzDW3d9scXlEpEnU2610eeuvv77369ev1cUQERERqWjq1KmvuXtNrycUWVnozqd0ef369WPKlCmtLoaIiIhIRWb2QqvLINIo6u1WREREREREGk7Bp4iIiIiIiDScgk8RERERERFpOD3zKSIiIiJFLVmyhLlz57JokToTr7fVV1+dvn370rNnz1YXRaRpFHyKiIiISFFz585l7bXXpl+/fphZq4vTZbg7r7/+OnPnzqV///6tLo5I06jZrYiIiIgUtWjRItZbbz0FnnVmZqy33nq6oyzdjoJPERERESlJgWdjaLlKd6TgU0RERERWGr/85S9555136pZORJpHwaeIiIiIrDQUfIqsvBR8ioiIiEin9J///If999+f7bffnu22245zzjmHl156iREjRjBixAgATjjhBNra2th2220ZM2YMAOPGjVsh3Z133skuu+zCsGHDOPTQQ3n77bdb9rtEuisFnyIiIiLSKd1+++1suummPPbYYzzxxBN8+9vfZtNNN2XixIlMnDgRgPPOO48pU6bw+OOPc++99/L4449z8sknL5futdde49xzz2XChAk88sgjtLW18fOf/7zFv06k+1HwKSIiIiKd0qBBg5gwYQKnnXYa9913H717914hzfXXX8+wYcMYOnQoTz75JDNmzFghzYMPPsiMGTPYbbfdGDJkCFdeeSUvvPBCM36CiGToPZ8iIiIi0ikNGDCAqVOncuutt3LGGWcwcuTI5cY///zz/PSnP+Xhhx+mT58+jB49uujrS9ydffbZh2uuuaZZRReRIhR8ikiX8OL0Ea0uQs02HzSx1UUQEenUXnrpJdZdd12OOuoo1lprLcaPH8/aa6/NwoULWX/99XnrrbdYc8016d27N6+88gq33XYbw4cPB1gu3c4778yJJ57IrFmz2GqrrXjnnXeYO3cuAwYMaO0PFOlmFHyKiIiISKc0ffp0vvvd77LKKqvQs2dPLr74Yh544AH2228/NtlkEyZOnMjQoUPZdttt2XLLLdltt90+mPb4449fLt348eM5/PDDee+99wA499xzFXyKNJm5e6vLINJQbW1tPmXKlFYXQxpMdz5FROrvqaeeYuDAga0uRpdVbPma2VR3b2tRkUQaSh0OiYiIiIiISMMp+BQREREREZGGU/ApIiIiIiIiDafgU2pmZleY2atm9kSRcaeYmZvZ+um7mdk4M5tlZo+b2bBM2qPNbGb6Ozoz/BNmNj1NM87MLA1f18zuSunvMrM+zfi9IiIiIiLScQo+pT3GA6MKB5rZ5sA+wJzM4P2ArdPf8cDFKe26wBhgJ2BHYEwmmLw4pc1Nl5vX6cDd7r41cHf6LiIiIiIiKwEFn1Izd58MvFFk1C+AU4FsF8oHAVd5eBBYx8w2AfYF7nL3N9x9PnAXMCqN+7C7P+DRFfNVwGczeV2ZPl+ZGS4iIiIiIp2cgk+pCzM7EPiXuz9WMGoz4MXM97lpWLnhc4sMB9jI3V8GSP83rNsPEBERkU5p3LhxDBw4kCOPPLLo+ClTpnDyyScDMH78eE466aSa8u/Xrx+vvfZah8spIpX1aHUBZOVnZmsA3wdGFhtdZJi3Y3itZTqeaLrLFltsUevkIiIiUkS936lczfuOL7roIm677Tb69+9fdHxbWxttbXotpsjKQHc+pR4+CvQHHjOz2UBf4BEz25i4c7l5Jm1f4KUKw/sWGQ7wSmqWS/r/aqkCuful7t7m7m0bbLBBB36aiIiItMrXv/51nnvuOQ488EAuuOACdt11V4YOHcquu+7KM888A8CkSZM44IADVph23rx5fP7zn2eHHXZghx124P777wfg9ddfZ+TIkQwdOpSvfe1rxFM+ItIMCj6lw9x9urtv6O793L0fEUAOc/d/A7cAX0693u4MLEhNZu8ARppZn9TR0EjgjjRuoZntnHq5/TLw5zSrW4Bcr7hHZ4aLiIhIF3TJJZew6aabMnHiRE444QQmT57MtGnTGDt2LN/73vfKTvutb32L73znOzz88MPceOONfOUrXwHgnHPO4ZOf/CTTpk3jwAMPZM6cOWXzEZH6UbNbqZmZXQMMB9Y3s7nAGHe/vETyW4FPA7OAd4BjANz9DTP7IfBwSjfW3XOdGJ1A9KjbC7gt/QH8GLjezI4jetQ9tI4/S0RERDqxBQsWcPTRRzNz5kzMjCVLlpRNP2HCBGbMmPHB97feeouFCxcyefJk/vSnPwGw//7706eP3twm0iwKPqVm7n54hfH9Mp8dOLFEuiuAK4oMnwJsV2T468BeNRZXREREuoCzzjqLESNGcNNNNzF79myGDx9eNv2yZct44IEH6NWr1wrj0ivERaTJ1OxWRERERDq9BQsWsNlm0QH++PHjK6YfOXIkF1544QffH330UQB23313fv/73wNw2223MX/+/PoXVkSKUvApIiIiIp3eqaeeyhlnnMFuu+3G0qVLK6YfN24cU6ZMYfDgwWyzzTZccsklAIwZM4bJkyczbNgw7rzzTvWKL9JEph6+pKtra2vzKVOmtLoY0mD17v6/Gap5xYCISCs99dRTDBw4sNXF6LKKLV8zm+rueneMdEm68ykiIiIiIiINp+BTREREREREGk7Bp4iIiIiIiDScgk8RERERERFpOAWfIiIiIiIi0nAKPkVERERERKThFHyKiIiIiNTovvvuY9ttt2XIkCG8++67JdMNHz4cvfJNJPRodQFEREREZOWwx1r71zW/e9/+a13za6bf//73nHLKKRxzzDGtLorISkN3PkVERESkU/rPf/7D/vvvz/bbb892223HddddR79+/XjttdcAmDJlCsOHDwfg7bff5phjjmHQoEEMHjyYG2+8EYDbb7+dYcOGsf3227PXXnt9kO+xxx7LDjvswNChQ/nzn/8MwJNPPsmOO+7IkCFDGDx4MDNnzixahssuu4zrr7+esWPHcuSRRzJp0iQOOOCAD8p90kknMX78+OYtKJGVhO58ioiIiEindPvtt7Ppppvy17/GHdIFCxZw2mmnFU37wx/+kN69ezN9+nQA5s+fz7x58/jqV7/K5MmT6d+/P2+88QYA5513HnvuuSdXXHEFb775JjvuuCN77703l1xyCd/61rc48sgjWbx4MUuXLuXWW29doQy9e/fmb3/7GwcccACHHHIIkyZNavzCEOkCdOdTRERERDqlQYMGMWHCBE477TTuu+8+evfuXTLthAkTOPHEEz/43qdPHx588EF23313+vfvD8C6664LwJ133smPf/xjhgwZwvDhw1m0aBFz5sxhl1124fzzz+eCCy7ghRdeoFevXjWVQUTKU/ApIiIiIp3SgAEDmDp1KoMGDeKMM85g7Nix9OjRg2XLlgGwaNGiD9K6O2a23PTFhuWG33jjjTz66KM8+uijzJkzh4EDB3LEEUdwyy230KtXL/bdd1/uueeeomUolC1TYblEJE/Bp4iIiIh0Si+99BJrrLEGRx11FKeccgqPPPII/fr1Y+rUqQAfPNcJMHLkSC688MIPvs+fP59ddtmFe++9l+effx7gg2a3++67L7/61a9wdwCmTZsGwHPPPceWW27JySefzIEHHsjjjz9etAyFPvKRjzBjxgzee+89FixYwN13392YBSKyktMznyIiIiLSKU2fPp3vfve7rLLKKvTs2ZOLL76Yd999l+OOO47zzz+fnXba6YO0Z555JieeeCLbbbcdq666KmPGjOHggw/m0ksv5eCDD2bZsmVsuOGG3HXXXZx11ll8+9vfZvDgwbg7/fr14y9/+QvXXXcdV199NT179mTjjTfmBz/4AQ8//PAKZSi0+eab84UvfIHBgwez9dZbM3To0GYuJpGVhuWu+Ih0VW1tba73a3V9L04f0eoi1GzzQRNbXQQRkbKeeuopBg4c2OpidFnFlq+ZTXX3thYVSaSh1OxWREREREREGk7Bp4iIiIiIiDScgk8RERERERFpOAWfIiIiIlKS+gdpDC1X6Y4UfIqIiIhIUauvvjqvv/66AqU6c3def/11Vl999VYXRaSp9KoVERERESmqb9++zJ07l3nz5rW6KF3O6quvTt++fVtdDJGmUvApIiIiIkX17NmT/v37t7oYItJFqNmtiIiIiIiINJyCTxEREREREWk4BZ8iIiIiIiLScAo+RUREREREpOEUfIqIiIiIiEjDKfgUERERERGRhlPwKSIiIiIiIg2n4FNEREREREQaTsGniIiIiIiINJyCT6mZmV1hZq+a2ROZYT8xs6fN7HEzu8nM1smMO8PMZpnZM2a2b2b4qDRslpmdnhne38z+YWYzzew6M/tQGr5a+j4rje/XnF8sIiIiIiIdpeBT2mM8MKpg2F3Adu4+GHgWOAPAzLYBDgO2TdNcZGarmtmqwK+B/YBtgMNTWoALgF+4+9bAfOC4NPw4YL67bwX8IqUTEREREZGVgIJPqZm7TwbeKBh2p7u/n74+CPRNnw8CrnX399z9eWAWsGP6m+Xuz7n7YuBa4CAzM2BP4IY0/ZXAZzN5XZk+3wDsldKLiIiIiEgnp+BTGuFY4Lb0eTPgxcy4uWlYqeHrAW9mAtnc8OXySuMXpPQiIiIiItLJKfiUujKz7wPvA7/PDSqSzNsxvFxexcpxvJlNMbMp8+bNK19oERERERFpOAWfUjdmdjRwAHCku+eCwrnA5plkfYGXygx/DVjHzHoUDF8urzS+NwXNf3Pc/VJ3b3P3tg022KCjP01ERERERDpIwafUhZmNAk4DDnT3dzKjbgEOSz3V9ge2Bh4CHga2Tj3bfojolOiWFLROBA5J0x8N/DmT19Hp8yHAPZkgV0REREREOrEelZOILM/MrgGGA+ub2VxgDNG77WrAXakPoAfd/evu/qSZXQ/MIJrjnujuS1M+JwF3AKsCV7j7k2kWpwHXmtm5wDTg8jT8cuB3ZjaLuON5WMN/rIiIiIiI1IXpxpF0dW1tbT5lypRWF0Ma7MXpI1pdhJptPmhiq4sgIiKdjJlNdfe2VpdDpBHU7FZEREREREQaTsGniIiIiIiINJyCTxEREREREWk4BZ8iIiIiIiLScAo+RUREREREpOEUfIqIiIiIiEjDKfgUERERERGRhlPwKSIiIiIiIg2n4FNEREREREQaTsGniIiIiIiINJyCTxEREREREWk4BZ8iIiIiIiLScAo+RUREREREpOEUfIqIiIiIiEjDKfgUERERERGRhlPwKSIiIiIiIg2n4FNEREREREQaTsGniIiIiIiINJyCTxEREREREWk4BZ8iIiIiIiLScAo+RUREREREpOEUfIqIiIiIiEjDKfgUERERERGRhlPwKSIiIiIiIg2n4FNEREREREQaTsGniIiIiIiINJyCTxEREREREWk4BZ8iIiIiIiLScAo+RUREREREpOEUfIqIiIiIiEjDKfgUERERERGRhlPwKSIiIiIiIg2n4FNEREREREQarkerCyAi0l3tsdb+rS5Cze59+6+tLoKIiIispHTnU2pmZleY2atm9kRm2LpmdpeZzUz/+6ThZmbjzGyWmT1uZsMy0xyd0s80s6Mzwz9hZtPTNOPMzMrNQ0REREREOj8Fn9Ie44FRBcNOB+52962Bu9N3gP2ArdPf8cDFEIEkMAbYCdgRGJMJJi9OaXPTjaowDxERERER6eQUfErN3H0y8EbB4IOAK9PnK4HPZoZf5eFBYB0z2wTYF7jL3d9w9/nAXcCoNO7D7v6AuztwVUFexeYhIiIiIiKdnIJPqZeN3P1lgPR/wzR8M+DFTLq5aVi54XOLDC83DxERERER6eQUfEqjWZFh3o7htc3U7Hgzm2JmU+bNm1fr5CIiIiIiUmcKPqVeXklNZkn/X03D5wKbZ9L1BV6qMLxvkeHl5rECd7/U3dvcvW2DDTZo948SEREREZH6UPAp9XILkOux9mjgz5nhX0693u4MLEhNZu8ARppZn9TR0EjgjjRuoZntnHq5/XJBXsXmISIiIiIinZze8yk1M7NrgOHA+mY2l+i19sfA9WZ2HDAHODQlvxX4NDALeAc4BsDd3zCzHwIPp3Rj3T3XidEJRI+6vYDb0h9l5iEiIiIiIp2cgk+pmbsfXmLUXkXSOnBiiXyuAK4oMnwKsF2R4a8Xm4eIiIiIiHR+anYrIiIiIiIiDafgU0RERERERBpOwaeIiIiIiIg0nIJPERERERERaTgFnyIiIiIiItJwCj5FRERERESk4RR8ioiIiIiISMMp+BQREREREZGGU/ApIiIiIiIiDafgU0RERERERBpOwaeIiIiIiIg0nIJPERERERERaTgFnyIiIiIiItJwCj5FRERERESk4RR8ioiIiIiISMMp+BQREREREZGGU/ApIiIiIiIiDafgU0RERERERBpOwaeIiIiIiIg0nIJPERERERERaTgFn92AmW1hZh+ukGZtM9uiWWUSEREREZHuRcFn9/A88K0KaU5O6UREREREROpOwWf3YOlPRERERESkJRR8Ss5GwH9aXQgREREREemaerS6ANIYZvblgkFDigwDWBXYAvgSML3hBRMRERERkW5JwWfXNR7w9NmBg9JfoVxz3HeAcxpfLBERERER6Y4UfHZdx6T/BlwB3Az8uUi6pcDrwAPu/maTyiYiIiIiIt2Mgs8uyt2vzH02s6OBm939qhYWSUREREREujEFn92Au49odRlERERERKR7U2+3IiIiIiIi0nAKPrsJM9vDzP5iZq+a2RIzW1rk7/1Wl1NERERERLomNbvtBsxsf6LDoVWBOcAzgAJNERERERFpGgWf3cPZwBJgf3e/s8VlERERERGRbkjNbruH7YDrFHiKiIiIiEirKPjsHt4G3mjGjMzsO2b2pJk9YWbXmNnqZtbfzP5hZjPN7Doz+1BKu1r6PiuN75fJ54w0/Bkz2zczfFQaNsvMTm/GbxIRERERkY5T8Nk93A3s0uiZmNlmwMlAm7tvRzxjehhwAfALd98amA8clyY5Dpjv7lsBv0jpMLNt0nTbAqOAi8xsVTNbFfg1sB+wDXB4SisiIiIiIp2cgs/u4TTgo2Z2pplZg+fVA+hlZj2ANYCXgT2BG9L4K4HPps8Hpe+k8Xul8h0EXOvu77n788AsYMf0N8vdn3P3xcC1Ka2IiIiIiHRy6nCoexgDPAmcAxxrZo8CbxZJ5+5+XJHhVXH3f5nZT4kedd8F7gSmAm+6e6533bnAZunzZsCLadr3zWwBsF4a/mAm6+w0LxYM36m95RURERERkeZR8Nk9jM587pf+inHyTWJrZmZ9iDuR/Yng9o9EE9li8wEodhfWywwvdqfeiwzDzI4HjgfYYostypZbREREREQaT8Fn99C/SfPZG3je3ecBmNmfgIZKpvkAAB37SURBVF2BdcysR7r72Rd4KaWfC2wOzE3NdHsTHSPlhudkpyk1fDnufilwKUBbW1vRAFVERERERJpHwWc34O4vNGlWc4CdzWwNotntXsAUYCJwCPGM5tHAn1P6W9L3B9L4e9zdzewW4A9m9nNgU2Br4CHijujWZtYf+BfRKdERTfptIiIiIiLSAQo+pW7c/R9mdgPwCPA+MI24+/hX4FozOzcNuzxNcjnwOzObRdzxPCzl86SZXQ/MSPmc6O5LAczsJOAOoifdK9z9yWb9PhERERERaT8Fn92AmVX90KO7z+nIvNx9DNHBUdZzRE+1hWkXAYeWyOc84Lwiw28Fbu1IGUVEREREpPkUfHYPsynRMU8BR9uEiIiIiIg0gAKN7uEqigef6wBDgI8Ak4BmPRsqIiIiIiLdjILPbsDdR5caZ2arAGcBXyc6/xEREREREam7Yu9NlG7E3Ze5+zlE09wft7g4IiIiIiLSRSn4lJy/AyNbXQgREREREemaFHxKzrrAmq0uhIiIiIiIdE0KPgUz2xv4IvBEq8siIiIiIiJdkzoc6gbM7J4So3oAmwO594CObU6JRERERESku1Hw2T0MLzHcgfnAHcBP3b1UkCoiIiIiItIhCj67AXdX82oREREREWkpBSUiIiIiIiLScLrz2Q2Z2YeB3sACd3+r1eUREREREZGuT3c+uwkzW9XMTjezWcRznrOB+WY2Kw3XhQgREREREWkYBRzdgJl9CLgd2IPoZOhF4GVgE6AfcB4wysxGuvviVpVTRERERES6Lt357B7+i+jx9q/AQHfv5+67uHs/4GPA/wGfSulERERERETqTsFn93AE8ATwWXefmR3h7v8EDgaeBI5sQdlERERERKQbUPDZPWwF3Obuy4qNTMNvAz7a1FKJiIiIiEi3oeCze1gMrFUhzZrAkiaURUREREREuiEFn93D48AhZrZBsZFmtj5wCPBYU0slIiIiIiLdhoLP7uFCYAPgITM7zsy2NLNeZtbfzI4B/pHGX9jSUoqIiIiISJelV610A+5+vZkNAU4HLi2SxID/cffrm1syERERERHpLhR8dhPu/j0zuwU4DhgK9AYWANOAK9z9gVaWT0REREREujYFn92Iuz8IPNjqcoiIiIiISPejZz67KDNbzcweMrO7zaxnmXQfSmkeLJdORERERESkIxR8dl1HAp8AfubuJV+h4u6LgZ8AO6ZpRERERERE6k7BZ9d1MPCcu99aKaG73w7MBA5teKlERERERKRbUvDZdQ0FJtWQfjIwpDFFERERERGR7k7BZ9e1PvBKDelfAdZrUFlERERERKSbU/DZdb0LrFVD+rWARQ0qi4iIiIiIdHMKPruuF4EdakjfBsxpUFlERERERKSbU/DZdU0CdjaztkoJzewTwK7AxEYXSkREREREuicFn13XhYADfzSzgaUSmdnHgT8CS4GLmlQ2ERERERHpZnq0ugDSGO7+jJmNBc4GppnZDcA9wFwiKO0L7AV8HlgN+IG7P9Oi4oqIiIiISBen4LMLc/exZvY+MAY4Aji8IIkBS4Dvu/uPml0+ERERERHpPtTstotz9/OBAcAPiWc6nwaeSZ/HAgPqGXia2TpmdoOZPW1mT5nZLma2rpndZWYz0/8+Ka2Z2Tgzm2Vmj5vZsEw+R6f0M83s6MzwT5jZ9DTNODOzepVdREREREQaR3c+uwF3f4G4+9kM/wvc7u6HmNmHgDWA7wF3u/uPzex04HTgNGA/YOv0txNwMbCTma2byttGNBGeama3uPv8lOZ44EHgVmAUcFuTfpuIiIiIiLST7nxK3ZjZh4Hd4f+3d+/hdtX1ncffHxJQRqp4iYgJLaiMSrGKTTEdxqrgJQIa7IgVtQKDUh1saccbOK03dKZaH3VaLwNCBBwtcrGaAjalSrQKIhFQblojYpOCEhtukZvgt3+s39HtyT7JOSdnnZ0j79fz7Ged/Vu/tdZ37XXy5Hz2b104BaCq7qmqW4BlwGmt22nAIe3nZcDp1fkasHOSXYHnARdU1YYWOC8AlrZ5D66qi6uqgNMH1iVJkiRpG2b41Ex6DLAe+HiSy5OcnORBwC5VdSNAmz6y9V9I9zzSMeta2+ba1w1plyRJkrSNM3xqJs0Hngp8tKr2AX5Cd4rtRIZdr1nTaN90xcnRSVYnWb1+/frNVy1JkiSpd4ZPzaR1wLqquqS9P5sujP6onTJLm9400H+3geUXATdsoX3RkPZNVNVJVbW4qhYvWLBgq3ZKkiRJ0tYzfGrGVNUPgbVJHt+aDgCuAVYAY3esPRz4XPt5BfDKdtfbJcCt7bTclcBzkzy03Rn3ucDKNu/2JEvaXW5fObAuSZIkSdsw73armfbHwCfbnW6vA46k+5LjzCRHAf8KHNr6ng8cCKwB7mh9qaoNSU4ALm393llVG9rPrwVOBXaku8utd7qVJEmS5gDDp2ZUVV1B94iU8Q4Y0reAYyZYz3Jg+ZD21cDeW1mmJEmSpFnmabeSJEmSpN4ZPiVJkiRJvTN8SpIkSZJ6Z/iUJEmSJPXO8ClJkiRJ6p3hU5IkSZLUO8OnJEmSJKl3hk9JkiRJUu8Mn5IkSZKk3hk+JUmSJEm9M3xKkiRJknpn+JQkSZIk9c7wKUmSJEnqneFTkiRJktQ7w6ckSZIkqXeGT0mSJElS7wyfkiRJkqTeGT4lSZIkSb0zfEqSJEmSemf4lCRJkiT1zvApSZIkSeqd4VOSJEmS1DvDpyRJkiSpd4ZPSZIkSVLvDJ+SJEmSpN4ZPiVJkiRJvTN8SpIkSZJ6Z/iUJEmSJPXO8ClJkiRJ6p3hU5IkSZLUO8OnJEmSJKl3hk9JkiRJUu8Mn5IkSZKk3hk+JUmSJEm9M3xqxiWZl+TyJOe293skuSTJd5N8OskOrf0B7f2aNn/3gXUc39q/k+R5A+1LW9uaJMfN9r5JkiRJmh7Dp/pwLHDtwPv3AB+oqj2Bm4GjWvtRwM1V9TjgA60fSfYCXgr8JrAU+EgLtPOADwPPB/YCDmt9JUmSJG3jDJ+aUUkWAQcBJ7f3AfYHzm5dTgMOaT8va+9p8w9o/ZcBZ1TV3VX1fWANsG97ramq66rqHuCM1leSJEnSNs7wqZn2QeBNwM/a+4cDt1TVve39OmBh+3khsBagzb+19f95+7hlJmqXJEmStI0zfGrGJDkYuKmqvjHYPKRrbWHeVNuH1XJ0ktVJVq9fv34zVUuSJEmaDYZPzaT9gBcmuZ7ulNj96UZCd04yv/VZBNzQfl4H7AbQ5j8E2DDYPm6Zido3UVUnVdXiqlq8YMGCrd8zSZIkSVtl/pa7SJNTVccDxwMkeSbwhqp6eZKzgBfTBdLDgc+1RVa09xe3+V+sqkqyAvhUkvcDjwb2BL5ON/K5Z5I9gH+juynRy2Zp9yT1aO2Vzxp1CVO225MuHHUJkiTNKYZPzYY3A2ckeRdwOXBKaz8F+ESSNXQjni8FqKqrk5wJXAPcCxxTVfcBJHkdsBKYByyvqqtndU8kSZIkTYvhU72oqlXAqvbzdXR3qh3f5y7g0AmWfzfw7iHt5wPnz2CpkiRJkmaB13xKkiRJknpn+JQkSZIk9c7wKUmSJEnqneFTkiRJktQ7w6ckSZIkqXeGT0mSJElS7wyfkiRJkqTeGT4lSZIkSb0zfEqSJEmSemf4lCRJkiT1zvApSZIkSeqd4VOSJEmS1DvDpyRJkiSpd4ZPSZIkSVLvDJ+SJEmSpN4ZPiVJkiRJvTN8SpIkSZJ6Z/iUJEmSJPXO8ClJkiRJ6p3hU5IkSZLUO8OnJEmSJKl3hk9JkiRJUu8Mn5IkSZKk3hk+JUmSJEm9M3xKkiRJknpn+JQkSZIk9c7wKUmSJEnqneFTkiRJktQ7w6ckSZIkqXeGT0mSJElS7wyfkiRJkqTeGT4lSZIkSb0zfEqSJEmSemf4lCRJkiT1zvCpGZNktyQXJrk2ydVJjm3tD0tyQZLvtulDW3uS/HWSNUm+leSpA+s6vPX/bpLDB9p/O8mVbZm/TpLZ31NJkiRJU2X41Ey6F3h9VT0RWAIck2Qv4DjgC1W1J/CF9h7g+cCe7XU08FHowirwNuBpwL7A28YCa+tz9MByS2dhvyRJkiRtJcOnZkxV3VhVl7WfbweuBRYCy4DTWrfTgEPaz8uA06vzNWDnJLsCzwMuqKoNVXUzcAGwtM17cFVdXFUFnD6wLkmSJEnbMMOnepFkd2Af4BJgl6q6EbqACjyydVsIrB1YbF1r21z7uiHtkiRJkrZxhk/NuCQ7AecAf1pVt22u65C2mkb7sBqOTrI6yer169dvqWRJkiRJPTN8akYl2Z4ueH6yqj7Tmn/UTpmlTW9q7euA3QYWXwTcsIX2RUPaN1FVJ1XV4qpavGDBgq3bKUmSJElbzfCpGdPuPHsKcG1VvX9g1gpg7I61hwOfG2h/Zbvr7RLg1nZa7krguUke2m409FxgZZt3e5IlbVuvHFiXJEmSpG3Y/FEXoF8p+wF/CFyZ5IrW9hbgL4EzkxwF/CtwaJt3PnAgsAa4AzgSoKo2JDkBuLT1e2dVbWg/vxY4FdgR+Hx7SZIkSdrGGT41Y6rqKwy/LhPggCH9CzhmgnUtB5YPaV8N7L0VZUrSjHjGTgeNuoQp+dLG80ZdgiTpfs7TbiVJkiRJvTN8SpIkSZJ6Z/iUJEmSJPXO8ClJkiRJ6p3hU5IkSZLUO8OnJEmSJKl3hk9JkiRJUu8Mn5IkSZKk3hk+JUmSJEm9M3xKkiRJknpn+JQkSZIk9W7+qAuQtiVrr3zWqEuYklf87n8adQlT9qWN5426BEmSJI2AI5+SJEmSpN4ZPiVJkiRJvTN8SpIkSZJ6Z/iUJEmSJPXO8ClJkiRJ6p3hU5IkSZLUO8OnJEmSJKl3hk9JkiRJUu8Mn5IkSZKk3hk+JUmSJEm9M3xKkiRJknpn+JQkSZIk9c7wKUmSJEnqneFTkiRJktQ7w6ckSZIkqXeGT0mSJElS7wyfkiRJkqTeGT4lSZIkSb0zfEqSJEmSemf4lCRJkiT1zvApSZIkSeqd4VOSJEmS1Lv5oy5AmqokS4H/C8wDTq6qvxxxSZJ0v7b2ymeNuoQp2e1JF466BEm6XzJ8ak5JMg/4MPAcYB1waZIVVXXNaCuTJM0Vz9jpoFGXMGVf2njeqEuQpK3mabeaa/YF1lTVdVV1D3AGsGzENUmSJEnaAsOn5pqFwNqB9+tamyRJkqRtmKfdaq7JkLbapFNyNHB0e7sxyXd6rWp0HgH8eNRFTEUy7BDeb3n85rY5dfw8dr9kTh078PiNM+eO3xT9xqgLkPpi+NRcsw7YbeD9IuCG8Z2q6iTgpNkqalSSrK6qxaOuQ9Pj8ZvbPH5zl8dubvP4SXOXp91qrrkU2DPJHkl2AF4KrBhxTZIkSZK2wJFPzSlVdW+S1wEr6R61sryqrh5xWZIkSZK2wPCpOaeqzgfOH3Ud24hf+VOLf8V5/OY2j9/c5bGb2zx+0hyVqk3u1SJJkiRJ0ozymk9JkiRJUu8Mn9IclWRpku8kWZPkuFHXo8lLsjzJTUmuGnUtmpokuyW5MMm1Sa5Ocuyoa9LkJXlgkq8n+WY7fu8YdU2amiTzklye5NxR1yJp6gyf0hyUZB7wYeD5wF7AYUn2Gm1VmoJTgaWjLkLTci/w+qp6IrAEOMZ/e3PK3cD+VfVk4CnA0iRLRlyTpuZY4NpRFyFpegyf0ty0L7Cmqq6rqnuAM4BlI65Jk1RVXwY2jLoOTV1V3VhVl7Wfb6f7I3jhaKvSZFVnY3u7fXt584s5Iski4CDg5FHXIml6DJ/S3LQQWDvwfh3+ASzNqiS7A/sAl4y2Ek1FO23zCuAm4IKq8vjNHR8E3gT8bNSFSJoew6c0N2VIm9/eS7MkyU7AOcCfVtVto65Hk1dV91XVU4BFwL5J9h51TdqyJAcDN1XVN0Zdi6TpM3xKc9M6YLeB94uAG0ZUi3S/kmR7uuD5yar6zKjr0fRU1S3AKrz+eq7YD3hhkuvpLjXZP8n/H21JkqbK8CnNTZcCeybZI8kOwEuBFSOuSfqVlyTAKcC1VfX+UdejqUmyIMnO7ecdgWcD3x5tVZqMqjq+qhZV1e50/+d9sapeMeKyJE2R4VOag6rqXuB1wEq6G56cWVVXj7YqTVaSvwUuBh6fZF2So0ZdkyZtP+AP6UZdrmivA0ddlCZtV+DCJN+i+xLvgqrykR2SNEtS5WVikiRJkqR+OfIpSZIkSeqd4VOSJEmS1DvDpyRJkiSpd4ZPSZIkSVLvDJ+SJEmSpN4ZPiVJI5OkkqwadR1zXZLVSTaOuo7ZluTs9jv0iFHXIknaMsOnJM2SJE9I8jdJrkpya5J7ktyQ5LwkRyV54KhrvD9L8vYWZCb7un6a29mpLT+y50u2sDq2H4dupt9fDfT70GzW2Lb/vrbtxbO9bUnSzJs/6gIk6f4gyVuBt9F96fc14DRgI7AL8EzgZOC1wP3tj+wnAneMuohm1ZC2pwDLgG8Cnx0375a+C5oF9wKvBs4aPyPJ9sDhrY9/L0iStpr/mUhSz5K8BXgHsBY4tKouGdLnYOD1s13bqFXVt0ddw5iqWsW4AJrkCLrweUVVvX3Wi+rfucCyJHtU1ffHzTsEWAD8HfCiWa9MkvQrx9NuJalHSXYH3g78FDhwWPAEqKpzgaXjlj0iyTlJrktyZ5Lbknw1ySsm2Naqdori9knemuR7Se5K8u0krx7o95okV7Z1rkvyjiTbjVvX7m1dp7bThT+bZEOSnyT5SpLnDtn+Q5K8MckX23rvSbI+yYokSyaoeeg1n0l2TfLxJDe1Oq9IcniSZ7Zl3j7Bvs9P8pYk301yd5K1Sd6TZIdh258pSeYl+ZMkl7XPaGOSryX57+P6vQ64vb09aNxpvG9ofZLk6CSfS/L9tv+3JPnS5k6RnaaPAQGOGjLv1cCtDBkVHdifh7VTY9e0z/vf22nkTx/S9+Cx/Uyyb5KV6U4/35jkn5L89rj+P+YXX8hcOvA5Dbu2NUmOTXJN+52/McmHkjxoSMfF6a4V/UGr+aZ0pyG/b+KPSZI0Exz5lKR+HQlsD5xRVVdtrmNV3T2u6aPANcCXgRuBhwMHAp9I8viq+osJVnUG8DTgfLrQ+2LgpCQ/BX6L7lTKc4EvAC8E3kp36ut7hqxrD+Bi4CrgRGBX4A+Azyd5WVV9eqDvE4F3t3rPA24Gfr1t4/lJXlBV/7C5zwAgySOBi4Dd27ouAh4FfAT4xy0s/ing6cDngdvoPq83AY+kOxYzrgX3c+hGSL9P9znNA34fOCXJkqo6unX/OvB/gOOB77Z6x1zUpvPaOr4OXAj8iG4E8mDgzCTHVdWwYzUdl7XXkUneVlX3tX3aHXg23Wd+57AFB47TY9v0LLrj9BJgaZIjquoTQxb9r8C76EaZPwY8hm6UdVWSvavqB63fe1v777Z+N7T2e4as88PAAXS/d/8APAc4BvgN4AUDNT8N+GfgbmAF8ANgZ+A/A8cCbxi2r5KkGVJVvnz58uWrpxddwCvgVdNY9rFD2nZo6/wpsHDcvFVtW5cCOw+0P4buD/ab6cLRwoF5OwM/BtYD8wfad2/rKuCvxm1ncdv+zcCDB9ofAjxiSM2L6ILDtUPmFbBqXNsprf0949qfTBcaCnj7BPv+DeBhA+0PAtYA9wGPmsYxOKKt99TN9Hl16/NVYMeB9gfThfYCXjjQvlNrO3eC9QV4zJD2HelC3p3Aw8fNWw1snMJ+rW41PAp4Tfv5BQPz39XankwXAAv40Lh1fLK1v39c+950X2bcAewy0H7wwO/Ui8ct8/rW/t5x7e9r7Ysn2I+z2/x/AXYd9+9kbB/3Gmg/sbUdMGRdm/zu+vLly5evmX152q0k9WvXNl031QWr6ntD2u6hG+WZTzfSM8xxVXXLwDLXAV+hC5onVNW/Dcy7Bfh74BHAwiHruhV457gaVtMFj50ZuBawqm6tqh8PqXkdXUh4QpJfn6BmANrpsYe17b5r3Hq+CZy+ueWBN1fVhoFlftJq3Y7+buY0dmrtG6vq56OEVXUb8L/a21dNdmXVuW5I+53A/wMeCDxj+uVu4lPAT+hCNEnm0Y0SX9o+800k2YluhHMD3cj5YJ1jo+Q7Ai8fsvjKqjp7XNtJbbrvNPfhrVV140AN99Dd1GuidW4ymjvsd1eSNLMMn5LUr7RpTXnB5NeTfLhds3nH2DVvdKd4wvCwCN2Iz3hjpyx+Y8i8sTC6aMi8y6rq9iHtq9p0n3E175fkzHat5d0DNf/xFmoe83i60PKtCbb7lS0sP2zf17bpQ7ew7HTtA9xFd3ryeF8c6DNpSR6b5MQk/zLu2I8Fqi19jpPWQvKZwIFJHk03QvloulNdJ/Ikui9ALq2qYddgbm6/NzlG7VjfyvSP0WSP+9/S/VtcmWR5kpcn2WOa25QkTZHXfEpSv24AnsDwYDehJI+hu+bvoXTXqP0j3R/n99GdEns48IBhy1bVrUOa723Tzc3bfsi8H01Q4g/b9CEDNb+IboTzLuAC4Ht0I2o/o3uczDMmqnnA2Pom2u5E7cDPR3LHG9u/eVvY9pSlezbrA4Drq2qTLxiq6vYkP6EbJZ7sOveiO712J7qQP3b96n101yYexpY/x6n6GN1o55F011hupAtqExk7TjdOMH+sfdh+T/SImnuZ/jGa1HGvqlVJ9gfeDLyMdh1wkquBv6iqv5vm9iVJk2D4lKR+fQXYn+4U2VOmsNz/pLvB0JFVdergjCSH0YXP2bDLBO2PatPBMHsC3bWli6vq2sHOSU5kcqeK3raF7U7UPhJVdVeSu5mgrnZ66oP4xejyZLyJLtwdOv701HR3LT5smuVOqKouTnIV3U16Hgksn2BEc8zYcX/UBPN3Hddvm1HtkTrti4Pfobsp1THA2UmeXlUXbW55SdL0edqtJPXr43Q35/lvbURrQkkGR7Me16bnDOk6k9f7bclTk/zakPZntunlA22PA64ZEjy3o7vD6WR8m+56vN+aYLuTXc9sugLYsd1Jdbz92/Sygbb72nSiUb7H0Y0Wf3bIvD6P/cfoQuM8Nn/KLcCVdCOLvzPscSbAs9r0siHzpmJLn9W0VdVdVfXPVXU83UjodgzcGVeSNPMMn5LUo6q6nu45nzsA5yUZetObJEvpTq8cc32bPnNcv+cxhZvXzICHMO6GMm0fXk43qjV4muL1wJ7tusGxvgHeBmw2eI9pN4r5dNvun4/b7pOBV055D/q3vE3fO/gFQgvPYzdN+vmod7tx0J10j6EZ5nq6/59/6VmZSQ6hO1W0Lx+nu4HUC6rq0s11bKOiZ9GNzv/SI3/alyx/RLePn9p06Sn59zbd7I2qJivdc2KHfakxNnJ9x0xsR5I0nKfdSlLPqup/J5lPF8IuTXIR7dEYdH/0/h6wJ79805SP0F2PdlaSc+hO29wbWEp3c5g/mKXyvwy8qo3qfZVfPOdzO+CP2s1qxnyA7m6sl7eafwrsRxc8/57JjyodRzdi+Ka23Yvadl9C9+zSQ+hGBrcVJ9Pt28HAVUlW8IvnfO5Gdwrr58Yt8wXg4PY5jY0i/lNVfQ34G7rP+LwkZwE30T3y5Nl0ge8lfexEu+nPsNHWifwZsAR4c5L96E4x34WuvgcCR1XVDzez/GSM3bjoA0n2pfvC456qeu801/fnwJIkF9I9duhOumffPo/ucUPLN7OsJGkrOfIpSbOgqt5JFx4/RDeqdyTwRuAguhvzvIqBU0qr6lt0py5eRHdN2mvpnhv5+3QBb7Z8H/gvdM/0fA1dsLgMOLCqPj3YsapOpNuvG+muSX053R1Hn8YUTr+sqh+1bZ4O/CZdyNkH+B90j02BX1wbOnJV9TO6EcM/o/tC4bV0jy35YZsOG6l+Dd3NmX6PbuTwBNrxr6pLgOfQfRmxjG4U8QF0vyuf6HFXpqQdp33pvnRYSPeszhfR3SDrgPHXKk9zG6vpPsOb6e6YfALjRuKn6IPAZ+i+7DmC7lrP3Vv7PoOPIZIkzbwMuTmfJOl+LsnudMHztKo6YqTFDEjybuAtwNKqWjnqeiRJ0uQ58ilJ2uYMXjc60PYk4E+ADcCXZr0oSZK0VbzmU5K0LVqdZA1wFd2zQvekO+10O+A1VXXXKIuTJElTZ/iUJG2LTqS7sdBhwK8BtwArgfe15zRKkqQ5xms+JUmSJEm985pPSZIkSVLvDJ+SJEmSpN4ZPiVJkiRJvTN8SpIkSZJ6Z/iUJEmSJPXO8ClJkiRJ6t1/AFBDzk7u82aoAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x265247fdd68>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Descriptions of Campaign Time x State\n",
"state failed successful\n",
"run_time_months \n",
"0 12266 12483\n",
"1 144415 102023\n",
"2 38944 18225\n",
"3 1976 1117\n",
"4 13 3\n"
]
}
],
"source": [
"#Use sns to plot the distribution of the run time \n",
"plt.figure(figsize = (10,6))\n",
"\n",
"colors = ['#fde725','#440154']\n",
"ax = sns.countplot(x='run_time_months', hue='state', \n",
" data=ks_data[ks_data['run_time_months'] < 10], palette= colors)\n",
"ax.set_title(\"Kickstarter Campaigns' Monthly Run Time by Success Status\", fontsize=30)\n",
"ax.set_xlabel(\"Campaign Total Months\", fontsize=20)\n",
"ax.set_ylabel(\"Count\", fontsize=20)\n",
"plt.show()\n",
"\n",
"print(\"Descriptions of Campaign Time x State\")\n",
"print(pd.crosstab(ks_data[ks_data['run_time_months'] < 5]['run_time_months'], ks_data.state))"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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RVKyO/hzYEcvjtkAzYISInIhtT74jVne8hpm67w1ciH1T/TA/gSMy3UhEfkuqXlwJPIl1Yhdi7fLhmPPpHbG6fB9VnZ0prgpyPFafL8c6sBOxfD0Iq1vqYd/vEqxti3NPOFcHeydPZ7uJmIVQv3D4kaoWYiHxMak6+HwReUxryY5pItIKGyxuFU59hdVh07C+4fbk6OOKyF3Yu454AWvTvwIaYPX1wdj3Vi6ODP3sd7F+XAnWj9sb68dtBQwRkbWq+se0ODbDHKlGY4E3sfb5C6yN2Qbrkx2CtdfpaTie1A5NP2JOwt8j1ZbuQuXb0o9j/x8nItep6pcFxHMlVj/dhCld12HfQzoz044bYsrV1zDL7xLsm90JqxdOxb75gVif8rK08Ldi3/dvSFnFD8K+9TglFXuc/KimPEpnBOacdzrWT4nq0v7YmGAL4BkR6a6qkzOkWbA+XuSv79uQ7inhGXbD6pe9se/jVRHppqpJfoz2BB7H8vNRrO+4FGiO9ccrQvxd/auCYbNRB/vuvwRexeqSr7G+xS5Yf/dorL4eLiJfqep/0uI4G3u++7Cy/jVwfoZ7TYwfiFmKvRzCgrVvL2JKoIbYOz4dK+f3ishKVS1nPSm2YuBVUvXJHKwsfILVQ8dg/bSnKT+GTo+rHtaG7hdOfY/1Wydj7dSBIU31sU0ZNsZWaSSRWAZUdV2wxrkeq/uOxfqn2TgJG5OBKUyTxzEV0ODFtUn7UVbLdB9ZNFRZ4hoQCzsgw/W6WGGLZB4CNskSV33g6Aznh8TC94ydF0xZE12bDGyfJe7pQWYx0DLhebYgw8xgrufMIN+WZOuGOlhlHsV5bRa5nmn5NR9ok+PeYyP5PNJZh5QF1BqCJUgGue1IzSKtBdrmyCfFZiSyWmbkWb7Oj8X3SmXiqqb8mQvsmkEuPjs0Cesg3Jn+rWGVYPx7yTirmnZPxbb8TZfZDKuEIpm/Z4mrB9Ao4V1silVsUTwHZZFL/EawRiOqa9aSYbYUqzxfSHu2EVnuV5ImV262Aau4343JnJRBJtLcrwG6J7yHjbPlR5HKZLwsDQ/n9o+dKzebjXVaFHg5HOe0tMFmzuPf6JYZZA7DFFiKWTB2zJFexTo6LTLIdSVlBbmINGubDHENyeNdDUm798vAphnkBsdkxhRYbi9Nuh6Ta05q9vKlSpSD+LNlfReYomx1TLZnFrmqsrTR8LxnZ5BpCPwvqexW8lupDksbxWZ20+voTbHNGSKZiZiS+6AM8R0ck5uW5Z7dSFkezgR2zyLXN5bfb1fi3Q1Ke8YvMt0Tq3uWk6qvO2eQGRO7Xq7di8mdE7vf5QWm+6C0dE8K3+4OBcRVNEubUK5mxGRGAA2zxFUfOCrD+VNi4b8DDkxIz56k9QGxfnbUFqwGzsgSdgdSljhrgFZp1/vF0pGxvxCT3Y80SxJsYBeloVtC2I2BfQosB1tSdszyFfC79GepQHw5Z/gzlIesqxCwwd3bsXec8bugbF3TJMc9i1aHV0cepb3X6DcSqJcmI5S1UnmfzFZ9v43JPAlsnkFGgD/F5P6Y490oNkDfv9BnjJXHuNVSxjFoAfHWx/phWa01sD7AN+G+c7LJErOOzeO+W8Tkl2FGEJnkWmJtR/Qet84g82jsvTxF5r5fvG3I+h1iisZIZgawYwaZvbE+ZiTXp7JlAFPGRu1uxn5DTDb67ldnSl85+QoUhniCl8f+/1sBBWtALPyADNfjjdE4KqAQisUxJBZHz3CuHtY4RufHkrx0IzIz+3eBH1Dicxb6w2Yysn5MlB8Y9c0jzrGRfB6yx8Xi/kMO2ZakOpflzFLT8mk5sFMR3k+8c57R/L8qfwXkz75Z5BqEyiGSm0ZaAxaTjX8zGfMk7Z6PJqR/e1JLbH4kQTmT4z1sHqsrsg3mctUFcSVY1rzEZgrjFe+ILHIlMZn7E+I7JEkOszxTYGp1l6+EsjQ8dn5OOPdsmvxeMflTwrlEpQ024xHVhUtIGPCk5ddDeZT9HglxPZQkR+WUNt8BW2WRi5u3rsz0zeVRbrcmpcAan5Cm+HKREypRDuLPNiTtWj1sVvEibGY2kns3Ib6qVNrcnRBXn3zkCnxH1aG0mQLUzSJ3elrZvzThvq/H5DJ1NCPFxwqgeY5nuCkWV0EKZMorbXonyF4Uk/tXhut98/luMetdxaxQMy5dyDPtd6alPfp9js3W/g5TNiX2Mymu0ia+1HpsrntnCF8HW8KcMz8S4jgpFv7KHLKtSS0xuTPt2tWxeNoXkI65IeykQvM4z/v0I/MymUWYVcBNwJFkmSBOi6tCSps809cylqaMS4ypOaVNdeVRXGkzlwwD9iAnmKVPxvKPTWR+F65NJ8OSszT5aOD8PWnLWSg/YL+gCM/ZKhbfsqp8p1nuH1d6ZBt/VERpc0Usvv45ZA+LyV6Rdm1nUuPF+WRYShmTjX8L5b5DbPwULdlcRYLhAmUVz2MzXK9wGcDaFsWUcxnbaKxejeIs50Yh069QR8Sbhr/LsGU/xSbubOtqTfDyni9ijlVHk9pV5RlMo5Zp6UdEZHrVQvJwmleNRI5Dd5c0x4YZ+AxbTlJMTg9/V5Ej/1V1Dla5gn2sSTylql9VMm1g1hkRuZYnVQUVyZ9Jqvpupguq+hNlTRDvVtVsDrfGx/5vk0UmTlYHWar6DVYhgs1+F+SUVVWXYbPLYDPDhXBM7P/bE+61iIqbmGaND1sKFL3rTO8zqhuaiO1UVtuIdoQ6XES2i50/K/xditWH+XAk1gAC/FOTneXdibULAMdIslPdyao6LuF6fFlSPmW6IoxU1e8zXQjtTbQUowG23LFChPL4RDjcX2ybzjIE090B4fBbbFlCMSjjlBmbwSnB6upoGcRsMi+DrQ6Svrs3sU4OFD/Pq4Phmn35zdux/1dj/lqykbU+D21KVCc/reWXdacTN0HP1Qbnw1RVfS3h+n2ktsw+Rso7VH4eGxAADMxwHRHpgFnbgXVmvy00sap6AWbynh7HLtg38CfsfX8hIleISAOqnngf96oC+rjdsIEEmDIxKT+yEfXjVmLWYVlR1VmYlRKUL0PxJQo5HRZnIAq/S1iGWyWo6ihMmfZB2qWtwvkrMQvab4Jj0MSNHaogfXMwRQMU3l+qKqolj9K4I/SBy6E24r01diq9LTuc1Djgds29zPbh8LcRqXonE8spu8FModSWMQoUp6xFdck8cixTV9WXSdXF6XXJUZgFINhkadLuWEn9CLDVANESq+dUNX3JYpzHSC0tPEhEGifI5lsGhoe/QsrhdDrnxP7Pa6e6Qn3aTMf8CWwOvCEiB6lqRdfzJRH55FiCDZ4qy1bYGrloJ577sWURudY2v4LNRrTG1jsOxRrIpDWPlSZ4j+4H7IP5F9mcVEFOZ2dSFX0mxocKrpj0CH+/BXpK8gZBYLMbALuJSMOE95c0gKs1FDl//pvjdt/E/n8vq1RZua2yShlLSHXAsvE6qbWd+5ChIg6d25MwxUpHzEJnMzKvv8+5E1OG+OtgPn8Avtbc/hjGApfkGf2PlO+8laKqq0TkO8wKJdP7fAWzWtkaeFNE/gw8n0MJXJ2MxNbU1sMGCLcGxXP/cP2xCtRj8U7My0mCqvqjiIzHOk2bYQPObO85o7IyRtznQK4yXVGq4953k+rMDMKWTMU5ktQOdyPy6FgWA8Vmxe5Q1ZXVcL90liZ1ntR8Mi3ClgsUO8+rg6T6PF5HzwpK7Xxk09/DAaTq2FUikstfWFwJ0TqHbD4kKghUdaWITMAUS5tjS3Nmxa6vDWv+h2CKkz6Y5VCceGc2SbmVF6p6l4g8iH1zR2B9wT0p21btBPwZOFFEDlfVpHa7YIKvjWj3oYWq+naSfBbifusq7LMvpCGK41ugdx79uKh+2l1E6sfqq1diMveI7SD4iKp+TH68gvm82ZZUWzomx/dREGr+xjpiz34M1pfthPkzi9gM8xN0nIgcrapJ/a68CZM7p2Llrx1Wx22aRbzC/aUqptryKEYuRWT8+j5p13rE/t8ijzpyx9j/rSmrNI8zqarHf8VAbHeoAdjy0FbYcqxsu9pWqqyJ7eQUKWu/xhT1uYItxdxnpLdHXWL/v5EjjomYAiXb7ogV6beqiLxCqt3pirlcyES+ZeAVzA9Pc+AsEbkmrnMQkY1I9Q+/wJYg5qRQpU1vbFDXFjO7HhsUN5V2ahi0uFuEw9lFUjg8SMrRz59U9co8w/0Oq9x3whwWHQj8JCITsVmz14HXi9XZDpX641RsNmyLHNcLcbaWleB0LtJCNiH/2fqIrTCT7kwUK61x52yNihRnVeVPuiO5dOIzDVllVfWnWEWZuOU48HEe39Xc2P/ltuAV28r9KTI4FsxCrveQiS0xM1ewyi8X+chELMzjHUTvPtP7/BM2K9AGU1g9AqwVkSlY3fAG5qOkRhp4Vf1CRF7HZhAHYLNSR5NyHD6iAtHFOzRz8pCfgyltorDZlDa5BkXxsp+rTFeUKr+3qr4tItEEx+ki8nst6xR6UOz/xC1ZK0jcKbNgytR9sAFDA2wJ2+uYL4DqJld9B8nfXW0n6fnyqsszyKa/h6ax/weQstbKh2IowubmFinXfsxKu34f8AdssmMQMaWNiGxMyhKlBJtwqzRBSflU+CEiW2CDhF5Y53m3INoFm3nPuHV5EWhEarBeqGPo+EAr/d3mw5ak+ka7Ulg/7lsAVf1ARG7BnMpvjk0WXC8in2Mz+m9hExrZxgc3YoqMVthEyChgTYa2tChK5tDujwu/aPDUARvon0zK8mA74FkRaaW5N5RIJEz0PYTVxflQSH+pKqnWPAok1jOq+q2IRIP29D5q09j/Q6kYSXVkrR6jQKmD+psoq4hMorJlbVdSyu8uVKwuSX/X8XxM7M8HRUsJ1r/KRCH91kxh08mrDIT03YONFXbEJgziCvZjSfXHH8jX2rKg5VHBVPVgUo1FU0xxs0sh8aURL0BJplEVIa6cyqaVK4eqlmAV1B2kTNgaYLMkVxA8Y4vIbyQP1WIePElKIbAMGwhegXUojscyOd0TddLyA8iuICmUyi4FSapIipXW+BKrCi9tSKAq8qciZtGVXiYYSPS4Hvgh9n+ZbyZo1l8lpbD5AnNU+2vMt85xpN7FjCBTSF0Tn4WqaJpzUal3GZbW7It1ZqJZ8Wi3jUuwhusbEbk+dAprghHhbwcR2YvU4G6Oqr6TMURm4ubQ+bzjeL2dZEpdrPJcCNV177vD32gXAQBEZGdSiq2xqvpREe85W1WfCb/RqjpcVc/GZpS/wwYOL4lIOWVsNVCTeV4d5Pt8lXkPlWmDi1EXVar9AFDbuee5cHh02lKUE0h15nPvplEgqrpUVV9X1T9gbdldscuHici+VXFfitPHrWwcRe3HqeoVWL79L3Z6V8wi+U6gRESeE5E9SCMsJe0G3Exq2UQ9bAD4a8yVwTciMqQq3BSo6ipVnaiqt6rqvqR8T4HVlb/KHjo3IrIn5qIgUtjMwnYKvQCzfD029osG87n6jdVKDeSR5jnhFdUz6XVMVdWRxRqjzCdVxjZLW8JeMJLaaTV6hrewHTYHYQrJqJzFd4OqbFkr5rsuZp+/qvqtFSkDD2JuRKCs9Wj8eB22o1VeFLzld9By9sKWJLTCtrJ8Q0R6quq8xMDJxJcX5K1gycG52Pa8LYGLRARVvThHGKBUQXWxiFyKVVDdMeubg7GGcztsFrtNuE9BiMiB2Kw4mKf+Q1V1QRbZ/TOdrybiBXusqqZvd14biJsbdxWRunkshUtkPcqffNgkt0iZyjO9U3gRVu7BfKcMyuZrR0SuynQ+T+IVbUXTXOUE0+CrReQazNpmf6xu6I0N0jfHZpO7BnP7Khl8JPA0Vp9ugSkXIyXBP7OGyEzcBDqfdxyvt6vSfHp9YCQ207Ip1khHCt2BpDpLlV7+kQ+qOltEzsZ852wT7ntkEaIu1DeeUxjx+vg0VX04q2TVUNn2I2I4tkQl8u30p3A+6syuoTj+I3KiqqtF5BLM6qZVON2b3Msos5H0TRSjj1vZOOJ58qqqVnqrZlV9CngqKKR7YH3lntiyGsHqmv3FtlWekxZ2KfB/InI1plyO2tKDsbpqC6wPvw/FqbOSnuMhEelJyg9Fb1JlsxAOUQtpAAAgAElEQVSuImUtd52qDskmGJbw1QQ56/BqziPJ4UohIqpn0uuY+HETLWx79ypDVZeIyAxSViLdMcVXZbk+/F0DHBn8x5QjLBEsFvF3fZ+qpisnKkIx+/w13m8NepLRmMLscBHZWVW/FJHm2HcDZqGW9yqlSnW21ByWHgx8GE7tjiludq5EnMtIObHbs0gWLF9hjUeUzotEpEIOlFV1tapOUNW/quqx2NrOgaTMmM8JS0YK5ZDY/1dlUwgEdku4VqWo6hJSH2mbIuVPsZmGra0E0wL3LUKc60X+5MnueeRbfEYs3Tl09C7WAL/JprAJVOZdLCGlbW+eh3w+MkVHVdep6mRVvUNV+2Ezasdiu1KAmdlXaUczS7pWYMv5wGY862Fa/ZEVjGp+7P98lsPFZYrhWHy9JXR0o6VKB4tI8/DtDQznFhGWa1RTep4l5eD5iGC2n4n48pxc1hm5nK07xSU+ACnE8WtlKWctkUMmWx3wEvBp+P9sMVpiy9DBltRUW/0R2rG4D8V0S7RifROLSfWhWiXIJRGfGC3ET9H3pNrWopYhVf1SVUep6iWq2gF7xsg/RSNSA8tMYdep6vuq+ndVPRnzKXcCKUv3I0SkoI0RKkjcX0plLRKjOnY+cF02IRFpROUtoOJUSR1ejXmUWM8E65RooJ1eT9R0HZkPcYXK6Vml8iTUnbuGwyezKWwCxRyjFPNdx/MxsT8f+lFNE0RqS781ckhcl9RGIGeTWlKWlwPiiErPkKnqfExxE2nO98AUN5Wp6CInUI1INeCVIqSzF2UVN4ke83PEt0pVH6Ts7knpFhZxE+hcg+T4WtesDtzCUoue+aSxgpSmNY8BfdSx2Y6Uc+daQ7BoiOfLVWI7tVSGms6fYrIlKQe/2YhbUP0v7Vr0LhYmrfUOS3K2zXY9F2GNZ+R3YwcRydXB7VnovYpJ6NQ8A1wTO31ANvkqZkTa8WsFWELGHTEmzsiK7dIXPetyCvO3kC8VqV9rkmiJVLSLwKGkOhsjNcsOGVVIfOBwYxaZ+Hedqy2vbTud/Nx5M/b/L2tg4uTgpIvBJ81+4XAZqT5XGUI7HVmZ7YHV33E/T9VigZZG3OdU+ux9Ub6J8NyRNXDjAi1z45s2VHhSKrStUT97RxGpsm9YVT/EBvVRfZ13W6iqa4MFz5DY6epoS5PKAaSeJZ9vL+ovfZLD2vbQPOKrSJtXLXV4FeZRYj2Tdj29jxqvI2tql8Rc/IPUJi19Q3+5MuQ1Rgnk468rrzKutptoNP7vWsmxf3y33FyrOLqQbGVYkX6rUHZiPr08FYyqjiXVBp4dxogDwvHXpJYJ50VRzJrDbEgvUo6jWgCvi0iSM58k4ltU/lEybAlZCEFx05OU87cLK6O4CZTE/k9XDMQr+1zmWfH1e0l+WM6nEgPhBCqS1vjyipskeVvfmuIfpDSte2O75+TVuRWRhiKSvoSkpvOn2KTvZFOKiGwLnBYOV1Deq3n0LraT5O0fr0m4li/xbZB/nU0o+Nk5Ldv1GqIk9n9llYYFobYzyX+wXW3+S46tXbPwPKlZuzNzrL8+n5S/hWcquywxBxWps2oMVf0fqd3azqKsj4RiOiDONz1vkdrys6uIHJFBZhmp2fxuIpLRVDl0QCrl88GpGKEfE+3Y05qKOSIuBp3C0vhsDCRlMfBMDgeL95PalegC4Mzw/zzy3E0jCRHJ1/ErwRdH3EIgfZezOaTS2itbfyLPtijex72xgD7ue0DkB+sQEeldwfBQth93c7H62ZkIPlGiJV2FtIUlsf8rHF5Etqng88UVYZl2u4vanjpBSZlE1F/K2m8Mk4r5bJCSd5tXA3V4Sez/YvR3LszmDzB8e7+JnXo6TeQ5UpbOZ+Ux4VftqOonWP0H9r4erWB9dYqU3RUrrzGKiOxGqp5NIipr+fStorqkLuYEuVCeI6XIGii28U02so4HAuOAaEVE3+BbKhsnkrLsGauq+WyYUBGiibummDuVSLH1YI6VCuUoWiUdU9xEGr49McXNDtlDZeUJbIkLmMZ2ZEKFU09E8l56ELSCvSiruPlHhnh3FJGhYlunZSSkKV74p6aJfBr7P5dlQ1yzd43Ydsrp9zuayq2tTaIiaX2SVHoPBB5OGryLyMYicqaI9KtkGvMmLOM6CVvCA+aH5XER2TVbmGCefSIwGTgj7XJN50+xOUVEyjXUIrIp8CipTvf9GaxponchwB8zxCEicj2Qa5vFfBhBasboXBE5JcP9GmK7fWxdhPvlhYjcIyLZvNZHnbD42t70uqHaUNW+qrpv+FV4e9iwFDBylNYI+47K7TgQBg5ReVhDxXdtqCgVqbNqmqjR3pHUzN87qjoji3xVc3Ps/2uzyESD5s0yyYQyfh/mK86pXq4ipUC4U0T6JwmLyG4i8lcRaZwkVwH+malvJCL7kWoD12EOV7MSfAZGu42cQMpX2gNFUvjeISIvisjhSda2oS93P6klGUtJ20o7WMRFM/jNyTDQDYOMUeRui0aRctJ/EPBAaMcypa1eumI1KMLikyKPiUgPsiAiLUSkTYY0RJasvYB/JQ2SwmTWWSJyUtr534jIsTneb39Su+RMjZ2vKyLDM6QtHrYYbWk/YLqIDMzRVxURuZiyy1UeyiBaSN9+hxB3+j03wtrXfCwtKtrmVboOr8Y8SqcFcHd6uQoKmz+Tsg6aTNr20EFhdUM4bAC8IDksWUSkm4hUd//9N6TGunsCE0Qk0QpGRFqLyONYnzf+vc4g5ST3OBHZO0PYHbCJ0HwUMVFZ215yW8/8nZSC8Mwwds7qkFpEtgj1Rhnlf/A99EQ43BFrZ8op7kTkHFI7DGYk1NdR+1MfeDKTUiyUi7gT+j8nxVsgI4BoZ7ULoiRSwKRdUWd/VXWepJwTN8fWsr4u5pz428TAZeNZGwbPE7DG71TMH8AorDL4EVuD2RnbxnYlNhucb/xfh3S+js1UXWD1ABfFzBcbAL8Ffisi/8O0drOwAeSW2Ad2ChD57xlHytw04gPM0/p2wGkisgBzbBd9WCtUNeoEjMbWBu6M7RE/U0Tux7Y9a4Rtt3d0ePansR16islr2K43APeLyK3AZ6S0nnNVdS5Yh0FEjsfyZ2fMydJhIvIYNqO8GHMitQtm5XIoVrn8ochpTkRVx4vIcVjltjnWKTxabCvkNzFLnJ+w/GmPOWptkiW6ms6fYjIFS/NdQVM/GsuzltjyjWi966fY4CCdO0k5Ur1ERDphz/w1luenYB2QmVhZL9d45IuqficigzGHlHUwBeHJ2Pe+BGvYz8LqmycwjTlU/S4152B+rGZgHYbp2MzOpiEt/UitkZ2DKTrXZ36HOWRsiQ0yZorIA1geb4KZlp5MaiLgWlWtUkWVqn4vIpOxstZLRIZj9diymEylZ+uLxCOYEiuu7KrQWuYi8zzWPrXHrG2OVNX0NvR2bFKiPnBF6LSPxpwF7oEptltig79qU8g7Zr0lIhdh6+U3Bh4RkcswRcPHWLvWCOvfHIA5BwW4pQi3fwZTyE8Vkfswk/b62ATOGaT6lbeoaj7byt+NTbBErCM1A11ZBFsK8AtggYiMDemdj7XVW2NOVY+n7PKC36jtEJjOUFJm9HeILW16CVOgtcPaop3I8U2o6hoROQGzeNsK+84OjfVxV2AWu1EfdxmxbdFDHKNE5CBMedQYeFNEXgzpmY/5MdkdW0ZyQLjHzFj4daF/NAEbIJ0C9An9uPdJ9eN2xZYiHIK1b+kWIV2w2eNFIvJSCPsVlo87YO8+2nVTKTuxJcB5wHkiMh0bO6S3pf1JKdNmU/HtySNaY+XqDhF5C7M8/QzrR2yKjVeOwTYVifinqr6WHhHWzkSDrwdF5Dbgc1L95TnBkgJsQBsNToeJyMGYpdxCrP48E8unV7AylLRCIZ6WoWEQ+hGpyckv0iYCilGHV2ceRawFXsCsCPcWs3z/AitPp5BS2KwEzs607ExVbxORLtj4sSkwUURewMZ9X4bn2hZrAw/BNtP5EPh9JdOeN6q6QkQOx+rUfUIaXhSRaZjCbS72HTbC+uWHYd9bOSs/VV0pIvdi47iNgLdj9fOaEG4ANn4dSfmJ6XRew8Y1AM+E/tXXpPrWU4PVJ6q6LIwl3sDGWr8FThaRyAAj2pq9OTZ+6hXSmGmy4VKsr7ktNo76QERGYO3a1tg32gcr9z9im4Bk6+//BTgKW67bjtS4bQrWTvUI7yFSDN1VFX3G0Fd9grLK4NdidUSFIsvrh1W2SliSm0N2V2wwG4WZDmwXuz4gdm1AQjwtsAzXHL9PMoQdErveM0v822ONWCT3D0DCtd3yuG/0ex1onOUe5yaEK0mT3RerCLPJf499RInPhi0Bi64PyTN/62KKp2z3LhcP1ri8muc7WoPtMlThfKrsD2usx1QgP98HDssQT7XnD6ahjWSb5vmNjs11Heuozkt4lrlA84R7nY81rNnCz8Qa8bHRuSzxDIiFSaoLLgtlKNv9ngj5HB3fniWeEjJ8exWVxRqJfMrS1Fz5VsmyHS9LwwuMY4dcZSfIbY918JOedzVwZZ7pzVX2c8piitas5SJNdkjsWrnvsiKy+ZbbtDB3xsIsBjYpYjmIpzfxvcbCnBIL814WmfNylPXhWDsdHd+XJZ6orpmbR7rylq3gO/pjLJ1X5xmmXizMq1lkHorJNKlMXDHZQTHZ0xLkjsEmhvKpixYAWxX47sqkB/i/PMqFVCD+D2NhxxQxz2/AFFj5vJ/oHZ2SI84bE8KvBa7GBoE5yxo2+Tc9j3TNyRJesG9/VR5xZHwuTMn0Rp7vZw1p9R02AMwn7LL0NGD9znzzZjKwa4HloC/Jfbf03ypMuVQ34Vt+JyH81Wnyf8lxv7cwxVvOug/bXCBbPOXqXypZh1dXHoV7jY+Vs61zvOMlQJ8c8QlmYbQyz/SXq5exfmzW91ukempjbFnRsjzT+T2mPG2YFk9DYn3uLL9/JOV3LK4tsHFAtnjKtUtYH3xKns+wEjgky707Yoq1bGFLMCXMu+F4YcK73RybpEpKyzpM8VwnSxyVLgOY/9f4PU8qJJ4qWcOqtn1VL1JrHNsCr4n5yqhIPB9hg8t+2Ez159gMxCpMk/8KNgNckNMrtd2vepGafbgA08KLqn6GKZ/OwhqlD7BKYi2m3fsIW0ZytKoerFnWwKnqPZhW8BmsQs7qdFJV38UK6x2YVnFVuOd0zGSro6qOyRa+MqiZIh+KaZknYJVConmyqs5X1UOwwdXdmHne4hBuKfZeH8MG+LuoarX7bwjpnKWqR2AWH9djyqkvsLL0E6Y9HofNQnZT1c6awfN6TeZPsVHVKZiFwh+x9C/DtOGTsQ55B03QAqvqXZjj7Sew97caGzy8g2nKu2iwzCpSeodiGvp/Yd/RKuAbrA7op6onUtaKYVG5SIrLDtgswb2Yki/6XlZg9d5obIans6qWVHFaqoVQX3bHLGpGk6rPlmLf/m1AG1W9OWskxU/TC1g5fASzDMu1RWhN8mrs/4dV9cesktXDY9jkCsA+kmGZsarejeX5k6S+8/mYn6QjVPVXWAfEqQFU9d/YLPIFWJ5E7doqUvXxMGy2cSfNbD1SyH1vwizuHgv3jO73LDaQ+pWGnmqexC0IiuaAWFX/gFnSnoC9h7ewwcAKrL5eglkGPIFZme6uqo/kiPMqTFk8BvgOe/Z5mLVCD1Utt2Q4Ia4PgQ6YAvUprI+7MsT5JbbDzOVkcbKvxhBM+fNnzNJ5Eak+2DTsfR5Oahe79Di+UtVemEXOPVi/Lb0fNwob/DdR1RFpUZyD9aNvwJQ/X2Ltwmrs/YzDlnK1SH+3od8ZWU/ch/U/0tvSp7G2totWYGvctPs8i0069A7pfBHrwy3HBmzLMaubF7A+8B6q+nvNskRPzQ9Fb6yv9C45+suqegW2g+QLmIVNVI++hpW7XtnGEBnoD1yIWYt/R8rKJtu9K1WHV1ceZbjvIqyOuQgbkyzEytVczIKojeawigjfx3WYhce1WFn8BnsHK7C66xVsXNAtjGeqHVVdqar/h1nanI/VBXOw97wG+6anY5O4/YEdVfVmTdsSPRwfir2zd7F+/Uosjx4DDlXVC8mjzVbb+bIrpkyajNUFiRbsqjoLG1f8Ehs3fxTCrcXqlCmY/5szgR1U9dUs8UzFLN6uw8beP4RnmYYpqTur6nRM0QkJ/X1VXaaqR2IT6o9i3/lK7Jufg41d91bVwZrsf62yvEvKT9B3FLjFe2RV4jjOzxwRiT72N1W1Z02mpSoI68WHhcPjVLWyJrqOUzRE5CFS67D3CopTx9mgEdvI4HPM4uNrbIKnQs4ZHcdZ/xGR8dgkzFpVrZHNG5z1AzH/bAswa6qnVfX4Gk5SIiLSB1PaAvxVVS8rJJ4q8xbvOI5TXQSnZ+eFw9WktlR1nBonWJmeEA7fc4WN45TSl9RuGg+4wsZxHMfJwQWkfPu8UZMJyZPzw1+lEv4MXWnjOE6tRkQ2y+QJP3a9Abb7QttwarRWwPG541QDV2LO7SFlDeY4GzTByibaoGA15l/DcRzH2UARke45dp86gVS78QO22UytJTjEPjocvqSqcwqNy83PHMep7TTCPP9Pw9Yfz8R8EWyG+QM4mdQubt9jnusdp8YQkZ2xXSk2wXwfRLMss8niW8JxNgREpANmWdMY8xkYbcV7v6p+UWMJcxzHcWoDNwFtRWQM5qfra8zIpCnmm6ZHTPa3xfLVVizCtvS/wJx4twSuwKyCFPPTUzCutHEcZ32hQ/hl43Ogr6rOq6b0OE42DsW2qY/zEzAwm3NLx9lAuIKUb6eIjym/lbTjOI6zYbINth13tq3JVwO/D462axt1SfmviXNr2NCmYFxp4zhObWc+tvtFH2ynuO2wCl2wHQWmYjshjFDVlTWVSMfJwtfAe8C17svGcUpZgynaXwCuV9XFNZwex3Ecp+Y5H1tOdChmXbMNZlm/BNsl9HXgrvVkZ9Zol6p/UH4ir8L47lGO49QY22yzjTZt2rSmk+E4juM4jpOTSZMmfaeq29Z0OhzH2bBwSxvHcWqMpk2bMnHixJpOhuM4juM4Tk5E5LOaToPjOBsevnuU4ziO4ziO4ziO4zhOLcSVNo7jOI7jOI7jOI7jOLUQV9o4juM4juM4juM4juPUQtynjeM4tYrVq1czb948Vq70jaBqko033pgmTZpQv379mk6K4ziO4ziO42ywuNLGcZxaxbx589h8881p2rQpIlLTydkgUVUWLlzIvHnzaNasWU0nx3Ecx3Ecx3E2WHx5lOM4tYqVK1fSuHFjV9jUICJC48aN3drJcRzHcRzHcWoYV9o4jlPrcIVNzeN54DiO4ziO4zg1jyttHMdxHMdxHMdxHMdxaiGutHEcZ4Nn4sSJXHLJJVV+n7Fjx/LOO+9U+X0cx3Ecx3Ecx/l54I6IHcfZ4OnSpQtdunSp8vuMHTuWzTbbjO7du1fZPVQVVaVOHdfJO47jOI7jOM76jvfqHcf5WVBSUkKrVq0YNGgQ7dq149RTT+XVV19l//33p0WLFrz33nu89957dO/enb322ovu3bvz4YcfAqZMOeqoowAYMmQIAwcOpGfPnjRv3pxhw4Yl3nfkyJF06NCBjh07cvrppwPwn//8h27durHXXntxyCGH8M0331BSUsLw4cO59dZb6dSpE+PGjWPBggUcf/zx7LPPPuyzzz68/fbbACxYsIBDDz2Uzp07c95557Hbbrvx3XffAfC3v/2Ndu3a0a5dO2677bbSZ2/dujUXXHABnTt35oYbbmDw4MGlabz33nu59NJLi/vCHcdxHMdxHMepeqJZWf/5z3/+q+7f3nvvrenMnDmz3Ll8+PTTT7Vu3bo6bdo0Xbt2rXbu3FnPOussXbdunT7zzDN6zDHH6JIlS3T16tWqqvrKK6/occcdp6qqb7zxhh555JGqqnrttdfqfvvtpytXrtQFCxbo1ltvratWrcp4z+nTp2vLli11wYIFqqq6cOFCVVVdtGiRrlu3TlVV7733Xr300ktL477llltKw/fv31/HjRunqqqfffaZtmrVSlVVL7zwQr3ppptUVfWFF15QQBcsWKATJ07Udu3a6fLly3XZsmXapk0bff/99/XTTz9VEdEJEyaoqury5cu1efPmpeneb7/9dNq0aRV+p4XmheM4juP8HAEmai3oP/nPf/7bsH6+PMpxNiBEpC4wEfhSVY8SkWbAKGBr4H3gdFVdJSINgJHA3sBC4GRVLQlxXAmcDawFLlHVl8L5PsDtQF3gPlX9U7U+HNCsWTPat28PQNu2benduzciQvv27SkpKWHJkiWceeaZfPTRR4gIq1evzhjPkUceSYMGDWjQoAHbbbcd33zzDU2aNCkn9/rrr3PCCSewzTbbALD11lsDMG/ePE4++WTmz5/PqlWraNasWcb7vPrqq8ycObP0eOnSpSxbtozx48czevRoAPr06cNWW20FwPjx4zn22GPZdNNNATjuuOMYN24cffv2ZbfddmPfffcFYNNNN+Xggw/mueeeo3Xr1qxevbr0vTiO4ziO4ziOs/7gShvH2bD4NTAL2CIc/xm4VVVHichwTBlzV/j7varuISL9gtzJItIG6Ae0BXYCXhWRliGufwCHAvOA/4nIs6qa0khUAw0aNCj9v06dOqXHderUYc2aNfzhD3+gV69ejB49mpKSEnr27Jkznrp167JmzZqMcqqacWvsiy++mEsvvZS+ffsyduxYhgwZkjH8unXrmDBhAg0bNiwXb7b7ZSNS5EQMGjSIm266iVatWnHWWWdlDVfT7H35yIznJ91yRjWnxHEcx3Ecx3FqH+7TxnE2EESkCXAkcF84FuBg4Mkg8k/gl+H/Y8Ix4XrvIH8MMEpVf1LVT4G5QNfwm6uqn6jqKsx655iqf6qKsWTJEnbeeWcARowYUen4evfuzeOPP87ChQsBWLRoUbn7/POf/yyV33zzzVm2bFnp8WGHHcYdd9xRejxlyhQADjjgAB5//HEAXn75Zb7//nsADjzwQJ555hl+/PFHfvjhB0aPHk2PHj0ypq1bt2588cUXPPLII/Tv37/Sz+o4juM4juM4TvXjShvH2XC4DbgCWBeOGwOLVTUyI5kH7Bz+3xn4AiBcXxLkS8+nhcl2vhwicq6ITBSRiQsWLKjsM1WIK664giuvvJL999+ftWvXVjq+tm3bctVVV3HQQQfRsWPHUme/Q4YM4cQTT6RHjx6lS6cAjj76aEaPHl3qiHjYsGFMnDiRDh060KZNG4YPHw7Atddey8svv0znzp154YUX2HHHHdl8883p3LkzAwYMoGvXrnTr1o1Bgwax1157ZU3fSSedxP7771+6vMpxHMdxHMdxnPULSTK3dxzn54GIHAUcoaoXiEhP4DLgLGCCqu4RZHYBxqhqexGZAfxCVeeFax9j1jTXhzAPhfP3A2MwBfAvVHVQOH860FVVL05KV5cuXXTixIllzs2aNYvWrVsX6cnXT3766Sfq1q1LvXr1mDBhAueff36pFU5FOOqooxg8eDC9e/cuKB3VkRe+PMpxHMdZXxCRSarapabT4TjOhoX7tHGcDYP9gb4icgSwMebT5jagkYjUC9Y0TYCvgvw8YBdgnojUA7YEFsXOR8TDZDvvVJDPP/+ck046iXXr1rHRRhtx7733Vij84sWL6dq1Kx07dixYYeM4juM4juM4Ts3jShvH2QBQ1SuBKwEiSxtVPVVEngBOwHzQnAn8OwR5NhxPCNdfV1UVkWeBR0Tkb5gj4hbAe4AALcJuVF9izopPqabHq3IWLlyYUfnx2muv0bhx46Lfr0WLFkyePLng8I0aNWLOnDlFTJHjOI7jOI7jODWBK20cZ8Pmd8AoEfkjMBm4P5y/H/iXiMzFLGz6AajqDBF5HJgJrAEuVNW1ACJyEfAStuX3A6o6o1qfpApp3LhxQcuTHMdxHMdxHMdxKoMrbRxnA0NVxwJjw/+fYL5q0mVWAidmCX8jcGOG82Mw/zaO4ziO4ziO4zhOEfDdoxzHcRzHcRzHcRzHcWohrrRxHMdxHMdxHMdxHMephbjSxnEcJ426devSqVMn2rVrx4knnsiPP/6YKN+zZ0/Sty4HGDFiBBdddBEAw4cPZ+TIzNtbAwwZMoShQ4dmvNa9e/cKpN5xHMdxHMdxnJ8L7tPGcZxazehJc4sa37F775FTpmHDhqWOh0899VSGDx/OpZdeWqn7/upXvyo47DvvvFOpezuO4ziO4ziOs37iljaO4zgJ9OjRg7lz51JSUkK7du1Kzw8dOpQhQ4aUHj/00EN0796ddu3a8d5775WLJ25JM2zYMNq0aUOHDh3o169fqczMmTPp2bMnzZs3Z9iwYaXnN9tsMwDGjh1Lz549OeGEE2jVqhWnnnoqqgrAmDFjaNWqFQcccACXXHIJRx11VFHfg+M4juM4juM41Y9b2jiO42RhzZo1vPDCC/Tp0yen7A8//MA777zDW2+9xcCBA5k+fXpW2T/96U98+umnNGjQgMWLF5eenz17Nm+88QbLli1jzz335Pzzz6d+/fplwk6ePJkZM2aw0047sf/++/P222/TpUsXzjvvPN566y2aNWtG//79C39ox3Ecx3Ecx3FqDW5p4ziOk8aKFSvo1KkTXbp0Ydddd+Xss8/OGSZSlBx44IEsXbq0jDImnQ4dOnDqqafy0EMPUa9eSnd+5JFH0qBBA7bZZhu22247vvnmm3Jhu3btSpMmTahTpw6dOnWipKSE2bNn07x5c5o1a1YmLY7jOI7jOI7jrN+4pY3jOE4acZ82EfXq1WPdunWlxytXrixzXUQSj+M8//zzvPXWWzz77LPccMMNzJgxA4AGDRqUytStW5c1a9aUC5tJJloi5TiO4ziO4zjOzwu3tHEcx8mD7bffnm+//ZaFCxfy008/8dxzz5W5/thjjwEwfvx4ttxyS7bccsuM8QowgyQAACAASURBVKxbt44vvviCXr168Ze//IXFixezfPnySqWtVatWfPLJJ5SUlJRJi+M4juM4juM46zduaeM4jpMH9evX55prrqFbt240a9aMVq1albm+1VZb0b17d5YuXcoDDzyQNZ61a9dy2mmnsWTJElSVwYMH06hRo0qlrWHDhtx555306dOHbbbZhq5du1YqPsdxHMdxHMdxagfiZvWO49QUXbp00YkTJ5Y5N2vWLFq3bl1DKVp/Wb58OZttthmqyoUXXkiLFi0YPHhwpeKsjrzY+/KRGc9PuuWMKr2v4ziO41QUEZmkql1qOh2O42xY+PIox3GcnwH33nsvnTp1om3btixZsoTzzjuvppPkOI7jOI7jOE4l8eVRjuM4PwMGDx5cacsax3Ecx3Ecx3FqF25p4ziO4ziO4ziO4ziOUwtxpY3jOI7jOI7jOI7jOE4txJU2juM4juM4juM4juM4tRBX2jiO4ziO4ziO4ziO49RCXGnjOI6TRt26denUqVPpr6SkhIkTJ3LJJZcAMGLECC666KKC41dVzj33XNq0aUP79u2ZMGFCRrkhQ4Zw5ZVXljk3ZcqU0m24jzjiCBYvXpwx3NChQwtOn+M4juM4juM4tQPfPcpxnFrN3pePLGp8k245I6dMw4YNmTJlSplzTZs2pUuXLkVJw/jx4/noo4+YMWMGK1asYNmyZRnl+vfvz+GHH87NN99cem7UqFGccsopAIwZM6Yo6XEcx3Ecx3Ecp3biljaO4zh5MHbsWI466qhy5wcMGMD5559Pr169aN68OW+++SYDBw6kdevWDBgwIGNcG220Ed988w2rV69mk002Yfvtt88ot+eee9KoUSP++9//lp57/PHH6devH2CKpO+++w6AG2+8kT333JNDDjmEDz/8sFT+448/pk+fPuy999706NGD2bNnA/DZZ5/Ru3dvOnToQO/evfn8888Lei+O4ziO4ziO41QdrrRxHMdJY8WKFaVLo4499tic8t9//z2vv/46t956K0cffTSDBw9mxowZfPDBB+UsdgC23357li5dyoABA1DVxLj79+/PqFGjAHj33Xdp3LgxLVq0KCMzadIkRo0axeTJk3n66af53//+V3rt3HPP5e9//zuTJk1i6NChXHDBBQBcdNFFnHHGGUybNo1TTz21dOmX4ziO4ziO4zi1B1faOI7jpBEtj5oyZQqjR4/OKX/00UcjIrRv357tt9+e9u3bU6dOHdq2bUtJSUk5+RNOOIHXXnuNTTbZhMGDBwNwwQUX8Pzzz5eT7devH08++STr1q1j1KhR9O/fv5zMuHHjOPbYY9lkk03YYost6Nu3LwDLly/nnXfe4cQTT6RTp06cd955zJ8/H4AJEyaULrM6/fTTGT9+fN7vx3Ecx3Ecx3Gc6sF92jiO41SSBg0aAFCnTp3S/6PjNWvWlJH99ttv+e6779hzzz25++67Of7447nuuuuYOHEit9xyS7m4d9llF5o2bcqbb77JU089ldVpsYiUO7du3ToaNWqU0donn/CO4ziO4ziO49QsbmnjOI5TjWy77baoKm+88QZ169blnnvu4fbbb6dz585suummGcP079+fwYMHs/vuu9OkSZNy1w888EBGjx5d6tT4P//5DwBbbLEFzZo144knngBs16qpU6cC0L1799JlVw8//DAHHHBAVTyu4ziO4ziO4ziVwJU2juM41YiI8NRTT3HVVVfRqVMnfvnLX3LHHXfw7rvv8uSTT2YMc+KJJzJjxoxSB8TpdO7cmZNPPplOnTpx/PHH06NHj9JrDz/8MPfffz8dO3akbdu2/Pvf/wZg2LBhPPjgg3To0IF//etf3H777cV/WMdxHMdxHMdxKoXkcoLpOI5TVXTp0kUnTpxY5tysWbNo3bp1DaXIiVMdeZFtS/d8tmZ3HMdxnOpERCapapeaTofjOBsWbmnjOI7jOI7jOI7jOI5TC3GljeM4juM4juM4juM4Ti3ElTaO4ziO4ziO4ziO4zi1EFfaOM4GgIhsLCLvichUEZkhIteF8yNE5FMRmRJ+ncJ5EZFhIjJXRKaJSOdYXGeKyEfhd2bs/N4i8kEIM0x8D2nHcRzHcRzHcZxKUa+mE+A4TrXwE3Cwqi4XkfrAeBF5IVy7XFXTty06HGgRft2Au4BuIrI1cC3QBVBgkog8q6rfB5lzgXeBMUAf4AUcx3Ecx3Ecx3GcgnBLG8fZAFBjeTisH35JW8cdA4wM4d4FGonIjsAvgFdUdVFQ1LwC9AnXtlDVCWpb0o0EflllD+Q4juM4juM4jrMB4Eobx9lAEJG6IjIF+BZTvPw3XLoxLIG6VUQahHM7A1/Egs8L55LOz8twfr1ERDj99NNLj9esWcO2227LUUcdVVB8gwYNYubMmcVKnuM4juM4juM4Gwi+PMpxNhBUdS3QSUQaAaNFpB1wJfA1sBFwD/A74Hogkz8aLeB8OUTkXGwZFbvuumvOdD83486cMhXhqLYX5JTZdNNNmT59OitWrKBhw4a88sor7Lxz4Tqo++67r+CwjuM4juM4juNsuLiljeNsYKjqYmAs0EdV54clUD8BDwJdg9g8YJdYsCbAVznON8lwPtP971HVLqraZdttty3CE1UNhx9+OM8//zwAjz76KP379y+9NmTIEIYOHVp63K5dO0pKSvjhhx848sgj6dixI+3ateOxxx4DoGfPnkycOBGAF198kc6dO9OxY0d69+5djU/kOI7jOI7jOM76hittHGcDQES2DRY2iEhD4BBgdvBFQ9jp6ZfA9BDkWeCMsIvUvsASVZ0PvAQcJiJbichWwGHAS+HaMhHZN8R1BvDv6nzGYtOvXz9GjRrFypUrmTZtGt26dcsZ5sUXX2SnnXZi6tSpTJ8+nT59+pS5vmDBAs455xyeeuoppk6dyhNPPFFVyXccx3Ecx3Ec52eAK20cZ8NgR+ANEZkG/A/zafMc8LCIfAB8AGwD/DHIjwE+AeYC9wIXAKjqIuCGEMf/gOvDOYDzgftCmI9Zz3eO6tChAyUlJTz66KMcccQReYVp3749r776Kr/73e8YN24cW265ZZnr7777LgceeCDNmjUDYOutty56uh3HcRzHcRzH+fngPm0cZwNAVacBe2U4f3AWeQUuzHLtAeCBDOcnAu0ql9LaRd++fbnssssYO3YsCxcuLD1fr1491q1bV3q8cuVKAFq2bMmkSZMYM2YMV155JYcddhjXXHNNqZyqYoZIjuM4juM4juM4uXFLG8dxnCwMHDiQa665hvbt25c537RpU95//30A3n//fT799FMAvvrqKzbZZBNOO+00LrvsslKZiP32248333yzVH7RokU4juM4juM4juNkwy1tHMdxstCkSRN+/etflzt//PHHM3LkSDp16sQ+++xDy5YtAfjggw+4/PLLqVOnDvXr1+euu+4qE27bbbflnnvu4bjjjmPdunVst912vPLKK9XyLI7jOI7jOI7jrH+IrYJwHMepfrp06aLRrkoRs2bNonXr1jWUIidOdeTF3pePzHh+0i1nVOl9HcdxHKeiiMgkVe1S0+lwHGfDwpdHOY7jOI7jOI7jOI7j1EJcaeM4juM4juM4juM4jlMLcaWN4ziO4ziO4ziO4zhOLcSVNo7jOI7jOI7jOI7jOLUQV9o4juM4juM4juM4juPUQlxp4ziO4ziO4ziO4ziOUwtxpY3jOE4adevWpVOnTqW/kpKSSsfZs2dP0rc3dxzHcRzHcRzHSaJeTSfAcRwniV5/37+o8b1x8ds5ZRo2bMiUKVOKel/HcRzHcRzHcZyK4pY2juM4ebBy5UrOOuss2rdvz1577cUbb7yReH7FihX069ePDh06cPLJJ7NixYqaTL7jOI7jOI7jOOshbmnjOI6TxooVK+jUqRMAzZo1Y/To0fzjH/8A4IMPPmD27NkcdthhzPl/9u48usrqXvz/exOmCDgDolYJCCIQOIQk0jJbC0ptFXECK1gRSnG6+i1L7s9eC9b23lpbrRNU64BcEZxQW/G2oEZEVAiYIAIy2DhUVBxAkEES9u+PHNIAYTQkB/J+rXVWnufz7L3PZyerrOWn+9l76dKdxseNG8chhxzCggULWLBgAVlZWdU2nwPR396+Z4fYWe1GVkMmkiRJUvWxaCNJ26no9ahZs2Zx1VVXAdCmTRtOPPFEli5dutP4zJkzufrqqwHo0KEDHTp0qNpJSJIkSTrg+XqUJO2BGONexQFCCPsrHUmSJEk1gEUbSdoDPXr04JFHHgFg6dKlvP/++5x88sl7FF+4cCELFiyottwlSZIkHZgs2kjSHhg5ciQlJSVkZmZy4YUX8tBDD1GvXr2dxn/+85+zbt06OnTowC233EJubm51T0GSJEnSAcY9bSSltD05oruyrVu3bodY/fr1eeihh/Y4np6ezuTJk/dDdpIkSZJqClfaSJIkSZIkpSCLNpIkSZIkSSnIoo0kSZIkSVIKsmgjSZIkSZKUgizaSJIkSZIkpSCLNpIkSZIkSSnIoo0kbefjjz/moosuomXLlrRt25Z+/fqxdOnSPe7/+OOPc8opp9C7d28KCgqYNm3afsxWkiRJ0sGqdnUnIEm7svrRSZU63uEDB+3yeYyR/v37M2TIECZPngxAQUEBn3zyCa1btwagpKSEtLS0nY5x//33c88999C7d28eeugh8vPz6devX+VNQpIkSVKNYNFGksp56aWXqFOnDiNGjCiLJRIJ8vLy6N27N82aNaOgoIBFixZxzjnn8MEHH7Bx40auueYahg8fzk033cSsWbP45z//Sb9+/XjyySfZsGEDs2bN4j//8z855phjuOaaawAIITBz5kwaNWpUXdOVJEmSlMIs2khSOQsXLqRz584VPpszZw4LFy4kIyMDgAceeIAjjzySDRs2kJOTw4ABA7jxxht58cUXufXWW8nOzqZjx47k5+dz1113AfCjH/2Iu+++m65du7Ju3Trq169fZXOTJEmSdGBxTxtJ2kO5ubllBRuAO+64g44dO9KlSxc++OADli1bttsxunbtynXXXccdd9zB6tWrqV3b2rkkSZKkilm0kaRy2rVrx7x58yp81qBBg7LrvLw8ZsyYwWuvvUZhYSGdOnVi48aNux1/9OjR/OUvf2HDhg106dKFJUuWVFrukiRJkg4uFm2kGiCEUD+EMCeEUBhCeDuEMDYZzwghvBFCWBZCmBJCqJuM10veL08+b15urP9Mxt8JIfQtFz8jGVseQhhd1XOsLKeddhqbNm3ivvvuK4vNnTuXl19+eZt2a9as4YgjjuCQQw5hyZIlvP766xWO16hRI9auXVt2v2LFCjIzM7n++uvJzs62aCNJkiRppyzaSDXDJuC0GGNHIAGcEULoAvwOuC3G2Ar4EhiabD8U+DLGeBJwW7IdIYS2wEVAO+AM4J4QQloIIQ24GzgTaAsMTLY94IQQmDp1KtOnT6dly5a0a9eOMWPGcOyxx27T7owzzqC4uJgOHTrwX//1X3Tp0qXC8Xr37s2iRYtIJBJMmTKF22+/nfbt29OxY0fS09M588wzq2JakiRJkg5AbqYg1QAxxgisS97WSX4icBqw9QzsCcAYYBxwdvIa4AngrhBCSMYnxxg3Af8MISwHcpPtlscY3wUIIUxOtl30bXPf3RHd+8Oxxx7LY489tkN82LBhZdf16tXj+eefr7B/Xl5e2fWRRx7J3Llzy+4vvPDCyktUkiRJ0kHNlTZSDZFcEVMAfApMB1YAq2OMxckmHwLHJa+PAz4ASD5fAxxVPr5dn53FK8pjeAghP4SQv2rVqsqYmiRJkiQdlCzaSDVEjLEkxpgAjqd0dcwpFTVL/gw7eba38YryuDfGmB1jzG7cuPHuE5ckSZKkGsqijVTDxBhXA3lAF+DwEMLW1ySPBz5KXn8IfAcg+fww4Ivy8e367CwuSZIkSdpHFm2kGiCE0DiEcHjyOh04HVgMvAScl2w2BHgmef1s8p7k8xeT++I8C1yUPF0qA2gFzAHmAq2Sp1HVpXSz4mf3/8wkSZIk6eDlRsRSzdAMmJA85akW8FiM8W8hhEXA5BDCzcCbwP3J9vcDE5MbDX9BaRGGGOPbIYTHKN1guBi4IsZYAhBCuBL4O5AGPBBjfLvqpidJkiRJBx+LNlINEGNcAHSqIP4u/z79qXx8I3D+Tsb6DfCbCuLTgGnfOllJkiRJEuDrUZK0g08++YRBgwbRokULOnfuzHe/+12mTp1a3WlJkiRJqmFcaSMppc3N3mEh0LeSkz9nl89jjJxzzjkMGTKESZMmAfDee+/x7LNu0SNJkiSparnSRpLKefHFF6lbty4jRowoi5144olcddVVFBUV0b17d7KyssjKymL27NkA5OXl0bNnTy644AJat27N6NGjeeSRR8jNzSUzM5MVK1YAsGrVKgYMGEBOTg45OTm8+uqrALz88sskEgkSiQSdOnVi7dq1VT9xSZIkSSnHlTaSVM7bb79NVlZWhc+aNGnC9OnTqV+/PsuWLWPgwIHk5+cDUFhYyOLFiznyyCNp0aIFl19+OXPmzOFPf/oTd955J7fffjvXXHMN1157Ld26deP999+nb9++LF68mFtvvZW7776brl27sm7dOurXr1+VU5YkSZKUoizaSNIuXHHFFcyaNYu6desyY8YMrrzySgoKCkhLS2Pp0qVl7XJycmjWrBkALVu2pE+fPgBkZmby0ksvATBjxgwWLVpU1uerr75i7dq1dO3aleuuu46LL76Yc889l+OPP74KZyhJkiQpVVm0kaRy2rVrx5NPPll2f/fdd/PZZ5+RnZ3NbbfdRtOmTSksLGTLli3brIipV69e2XWtWrXK7mvVqkVxcTEAW7Zs4bXXXiM9PX2b7xw9ejQ//OEPmTZtGl26dGHGjBm0adNmf05TkiRJ0gHAPW0kqZzTTjuNjRs3Mm7cuLLY+vXrAVizZg3NmjWjVq1aTJw4kZKSkr0au0+fPtx1111l9wUFBQCsWLGCzMxMrr/+erKzs1myZEklzESSJEnSgc6ijSSVE0Lg6aef5uWXXyYjI4Pc3FyGDBnC7373O0aOHMmECRPo0qULS5cupUGDBns19h133EF+fj4dOnSgbdu2jB8/HoDbb7+d9u3b07FjR9LT0znzzDP3x9QkSZIkHWBCjLG6c5BUQ2VnZ8etG/lutXjxYk455ZRqykjlVcXfovOohyuMj7103Q6xs9qN3K+5SJK0KyGEeTHG7OrOQ1LN4kobSZIkSZKkFGTRRpIkSZIkKQVZtJEkSZIkSUpBFm0kSZIkSZJSkEUbSZIkSZKkFGTRRpIkSZIkKQVZtJGk7aSlpZFIJGjXrh0dO3bkj3/8I1u2bNmnsfr168fq1asrOUNJkiRJNUHt6k5AknblsxdvqtTxjj7txt22SU9Pp6CgAIBPP/2UQYMGsWbNGsaOHbvX3zdt2rS97iNJkiRJ4EobSdqlJk2acO+993LXXXcRY6SkpIRRo0aRk5NDhw4d+POf/wzAypUr6dGjB4lEgvbt2/PKK68A0Lx5cz777DMAfv3rX9OmTRt+8IMfMHDgQG699VYAevXqxfXXX09ubi6tW7cu6ytJkiSpZnOljSTtRosWLdiyZQuffvopzzzzDIcddhhz585l06ZNdO3alT59+vDUU0/Rt29fbrjhBkpKSli/fv02Y+Tn5/Pkk0/y5ptvUlxcTFZWFp07dy57XlxczJw5c5g2bRpjx45lxowZVT3N/WrqvOXVnYIkSZJ0wLFoI0l7IMYIwD/+8Q8WLFjAE088AcCaNWtYtmwZOTk5XHbZZWzevJlzzjmHRCKxTf9Zs2Zx9tlnk56eDsCPfvSjbZ6fe+65AHTu3JmioqL9PBtJkiRJBwKLNpK0G++++y5paWk0adKEGCN33nknffv23aHdzJkzee6557jkkksYNWoUgwcPLnu2teizM/Xq1QNKN0EuLi6u3AlIkiRJOiC5p40k7cKqVasYMWIEV155JSEE+vbty7hx49i8eTMAS5cu5euvv+a9996jSZMmDBs2jKFDhzJ//vxtxunWrRt//etf2bhxI+vWreO5556rjulIkiRJOoC40kaStrNhwwYSiQSbN2+mdu3aXHLJJVx33XUAXH755RQVFZGVlUWMkcaNG/P000+Tl5fH73//e+rUqUPDhg15+OGHtxkzJyeHH//4x3Ts2JETTzyR7OxsDjvssOqYniRJkqQDRNjdkn1J2l+ys7Njfn7+NrHFixdzyimnVFNG+9e6deto2LAh69evp0ePHtx7771kZWVVd1o7VZl/i51tRHzz5NkVxsdeum6H2FntRlZKLpIk7YsQwrwYY3Z15yGpZnGljSRVkeHDh7No0SI2btzIkCFDUrpgI0mSJKn6WbSRpCoyadKk6k5BkiRJ0gHEjYglSZIkSZJSkEUbSZIkSZKkFGTRRpIkSZIkKQVZtJEkSZIkSUpBFm2kGiCE8J0QwkshhMUhhLdDCNck42NCCP8KIRQkP/3K9fnPEMLyEMI7IYS+5eJnJGPLQwijy8UzQghvhBCWhRCmhBDqVu0sK8/HH3/MRRddRMuWLWnbti39+vVj6dKl32rM8ePH8/DDD1dShpIkSZJqAk+PkmqGYuD/xRjnhxAaAfNCCNOTz26LMd5avnEIoS1wEdAOOBaYEUJonXx8N/AD4ENgbgjh2RjjIuB3ybEmhxDGA0OBcd828aIxGd92iG00H/PPXT6PMdK/f3+GDBnC5MmTASgoKOCTTz6hdevWu+y7KyNGjNjnvpIkSZJqJlfaSDVAjHFljHF+8notsBg4bhddzgYmxxg3xRj/CSwHcpOf5THGd2OM3wCTgbNDCAE4DXgi2X8CcM7+mc3+9dJLL1GnTp1tiiyJRIJu3boxatQo2rdvT2ZmJlOmTAEgLy+Pnj17csEFF9C6dWtGjx7NI488Qm5uLpmZmaxYsQKAMWPGcOutpbWx5cuXc/rpp9OxY0eysrJYsWIFK1eupEePHiQSCdq3b88rr7xS9ZOXJEmSlFIs2kg1TAihOdAJeCMZujKEsCCE8EAI4Yhk7Djgg3LdPkzGdhY/ClgdYyzeLl7R9w8PIeSHEPJXrVpVCTOqXAsXLqRz5847xJ966ikKCgooLCxkxowZjBo1ipUrVwJQWFjIn/70J9566y0mTpzI0qVLmTNnDpdffjl33nnnDmNdfPHFXHHFFRQWFjJ79myaNWvGpEmT6Nu3b9l3JBKJ/T5XSZIkSanNoo1Ug4QQGgJPAv8RY/yK0teXWgIJYCXwh61NK+ge9yG+YzDGe2OM2THG7MaNG+/lDKrPrFmzGDhwIGlpaTRt2pSePXsyd+5cAHJycmjWrBn16tWjZcuW9OnTB4DMzEyKioq2GWft2rX861//on///gDUr1+fQw45hJycHB588EHGjBnDW2+9RaNGjap0fpIkSZJSj0UbqYYIIdShtGDzSIzxKYAY4ycxxpIY4xbgPkpff4LSlTLfKdf9eOCjXcQ/Aw4PIdTeLn7AadeuHfPmzdshHmOFNSgA6tWrV3Zdq1atsvtatWpRXFy8TdudjdOjRw9mzpzJcccdxyWXXOKmxZIkSZIs2kipKoRwQgjh0N20aRRCOGEPxgrA/cDiGOMfy8WblWvWH1iYvH4WuCiEUC+EkAG0AuYAc4FWyZOi6lK6WfGzsbQS8RJwXrL/EOCZPZlnqjnttNPYtGkT9913X1ls7ty5HHHEEUyZMoWSkhJWrVrFzJkzyc3N3cVIFTv00EM5/vjjefrppwHYtGkT69ev57333qNJkyYMGzaMoUOHMn/+/EqbkyRJkqQDk0UbKXX9E7hmN22uTrbbna7AJcBp2x3vfUsI4a0QwgKgN3AtQIzxbeAxYBHwf8AVyRU5xcCVwN8p3cz4sWRbgOuB60IIyynd4+b+vZhrygghMHXqVKZPn07Lli1p164dY8aMYdCgQXTo0IGOHTty2mmnccstt3DMMcfs03dMnDiRO+64gw4dOvC9732Pjz/+mLy8PBKJBJ06deLJJ5/kmmt296eXJEmSdLALu1ryL6n6hBC2AGNijDftos0NwE0xxrSqy6zyZGdnx/z8/G1iixcv5pRTTqmmjFReZf4tps5bXmH85smzK4yPvXTdDrGz2o2slFwkSdoXIYR5Mcbs6s5DUs3iShvpwNYU+Lq6k5AkSZIkVb7au28iqaqEEAZvF0pUEANIA06g9JWnt/Z7YpIkSZKkKmfRRkotD/Hvo7IjcHbys72tR2yvB8bu/7QkSZIkSVXNoo2UWn6a/BmAB4CnqfgUphLgc+C1GOPqKspNkiRJklSFLNpIKSTGOGHrdQhhCPB0jPHhakxJkiRJklRNLNpIKSrG2Lu6c5AkSZIkVR9Pj5Kk7aSlpZFIJMo+RUVFez1Gw4YNK4yPHz+ehx928ZQkSZKk3XOljZTCQgg9gVFALnAEFRdaY4zxoP3f8hvPV+7hWKeembnbNunp6RQUFFTq9241YsSI/TKuJEmSpIOPK22kFBVC+CEwA+hH6SlRrwMzK/i8Ul051iRFRUV0796drKwssrKymD17NgArV66kR48eJBIJ2rdvzyuv/PvPccMNN9CxY0e6dOnCJ598AsCYMWO49dZbASgoKKBLly506NCB/v378+WXXwLQq1cvrr/+enJzc2nduvU2Y0qSJEmqOSzaSKlrDLAZOCPG2DzG2D3G2LuiTzXnedDZsGFD2atR/fv3B6BJkyZMnz6d+fPnM2XKFK6++moAJk2aRN++fSkoKKCwsJBEIgHA119/TZcuXSgsLKRHjx7cd999O3zP4MGD+d3vfseCBQvIzMxk7Nh/n95eXFzMnDlzuP3227eJS5IkSao5DtpXKqSDQHtgcozxH9WdSE1T0etRmzdv5sorr6SgoIC0tDSWLl0KQE5ODpdddhmbN2/mnHPOKSva1K1bl7POFwv3rgAAIABJREFUOguAzp07M3369G3GW7NmDatXr6Znz54ADBkyhPPPP7/s+bnnnlvWd1/21JEkSZJ04HOljZS61gFfVHcSKnXbbbfRtGlTCgsLyc/P55tvvgGgR48ezJw5k+OOO45LLrmkbJPhOnXqEEIASjc2Li4u3qvvq1ev3j73lSRJknRwsGgjpa4XgO9WdxIqtWbNGpo1a0atWrWYOHEiJSUlALz33ns0adKEYcOGMXToUObPn79H4x122GEcccQRZfvVTJw4sWzVjSRJkiSBr0dJqex6YE4I4ZfAb2KMsboTqslGjhzJgAEDePzxx+nduzcNGjQAIC8vj9///vfUqVOHhg0b7tVx3hMmTGDEiBGsX7+eFi1a8OCDD+6v9CVJkiQdgIL/HSilphDCA0BzoCfwHlAArK6gaYwxDq3C1CpNdnZ2zM/P3ya2ePFiTjnllGrKSOVV5t9i6rzlFcZvnjy7wvjYS9ftEDur3ci9+s652bkVxnPy5+zVOJIkAYQQ5sUYs6s7D0k1iyttpNR1abnr5slPRSJwQBZtJEmSJEk7Z9FGSl0Z1Z2AJEmSJKn6WLSRUlSM8b3qzkGSJEmSVH08PUqSJEmSJCkFudJGSlEhhBP2tG2M8f39mYskSZIkqepZtJFSVxGlmwzvTsT/LUuSJEnSQcf/0JNS18NUXLQ5HEgAJwJ5lB4Hrkry+eef8/3vfx+Ajz/+mLS0NBo3bkxRURHHHnssixYt+tbfkZeXR926dfne9773rceSJEmSdPCyaCOlqBjjpTt7FkKoBfwXMAIYUlU5VYcr+t1WqePdPe3aXT4/6qijKCgoAGDMmDE0bNiQX/ziFxQVFXHWWWdVSg55eXk0bNhwr4o2xcXF1K7tP9mSJElSTeJGxNIBKMa4JcY4ltJXqP6nmtOpMUpKShg2bBjt2rWjT58+bNiwAYAVK1Zwxhln0LlzZ7p3786SJUsA+Otf/8qpp55Kp06dOP300/nkk08oKipi/Pjx3HbbbSQSCV555RVWrVrFgAEDyMnJIScnh1dffRUoLRoNHz6cPn36MHjw4GqbtyRJkqTq4f9tKx3YZgP+13wVWbZsGY8++ij33XcfF1xwAU8++SQ/+clPGD58OOPHj6dVq1a88cYbjBw5khdffJFu3brx+uuvE0LgL3/5C7fccgt/+MMfGDFiRNkKHoBBgwZx7bXX0q1bN95//3369u3L4sWLAZg3bx6zZs0iPT29OqcuSZIkqRpYtJEObEcCDao7iZoiIyODRCIBQOfOnSkqKmLdunXMnj2b888/v6zdpk2bAPjwww+58MILWblyJd988w0ZGRkVjjtjxoxt9sr56quvWLt2LQA//vGPLdhIkiRJNZRFG+kAFUI4HbgQWFjdudQU9erVK7tOS0tjw4YNbNmyhcMPP7xsH5zyrrrqKq677jp+/OMfk5eXx5gxYyocd8uWLbz22msVFmcaNLAmJ0mSJNVU7mkjpagQwos7+cwMIfwT+DtQD7ipmlOt0Q499FAyMjJ4/PHHAYgxUlhYCMCaNWs47rjjAJgwYUJZn0aNGpWtpAHo06cPd911V9l9RQUgSZIkSTWPRRspdfXayacr0IjSos0PYox/q4bcVM4jjzzC/fffT8eOHWnXrh3PPPMMULqR8Pnnn0/37t05+uijy9r/6Ec/YurUqWUbEd9xxx3k5+fToUMH2rZty/jx46trKpIkSZJSSIgxVncOkvazEMJ3gIeBY4AtwL0xxj+FEI4EpgDNKT2J6oIY45chhAD8CegHrAcujTHOT441BPhlcuibY4wTkvHOwENAOjANuCbu5h+Y7OzsmJ+fv01s8eLFnHLKKd92yqoElfm3mDpveYXxmyfPrjA+9tJ1O8TOajdyr75zbnZuhfGc/Dl7NY4kSQAhhHkxxuzqzkNSzeJKG6lmKAb+X4zxFKALcEUIoS0wGnghxtgKeCF5D3Am0Cr5GQ6MA0gWeX4FnArkAr8KIRyR7DMu2XZrvzOqYF6SJEmSdNByI2LpABFCOBQ4DFgTY/xqb/rGGFcCK5PXa0MIi4HjgLMpfeUKYAKQB1yfjD+cXCnzegjh8BBCs2Tb6THGL5I5TQfOCCHkAYfGGF9Lxh8GzgGe39f5SjXFzlYh9e98UhVnIkmSpFTjShsphYUQ0kIIo0MIy4EvKX2F6csQwvJkfK8LryGE5kAn4A2gabKgs7Ww0yTZ7Djgg3LdPkzGdhX/sIK4JEmSJGkfudJGSlEhhLrA/wE9gUhpsWQl0IzSPWh+Q+kqlz4xxm/2cMyGwJPAf8QYvyrduqbiphXE4j7EK8phOKWvUXHCCSfsLmVJkiRJqrFcaSOlrusofR3pOeCUGGPzGON3Y4zNgZOBvwLdk+12K4RQh9KCzSMxxqeS4U+Srz2R/PlpMv4h8J1y3Y8HPtpN/PgK4juIMd4bY8yOMWY3btx4T1KXJEmSpBrJoo2UugYBC4FzYozLyj+IMa4AzgXeBi7e3UDJ06DuBxbHGP9Y7tGzwJDk9RDgmXLxwaFUF0r30VlJ6THjfUIIRyQ3IO4D/D35bG0IoUvyuwaXG0uSJEmStA8s2kip6yTg+RjjlooeJuPPAy33YKyuwCXAaSGEguSnH/A/wA9CCMuAHyTvofTI7neB5cB9wMjkd34B/BqYm/zctHVTYuDnwF+SfVZwgG9CPHXqVEIILFmypMLnq1ev5p577qnirCRJkiTVJO5pI6Wub4CGu2nTANi8u4FijLOoeN8ZgO9X0D4CV+xkrAeAByqI5wPtd5fL3lq79tFKHa9Ro4F71O7RRx+lW7duTJ48mTFjxmzzrKSkpKxoM3LkyErNT/vfZy/eVGH86NNurOJMJEmSpF1zpY2UuhYA54UQKtz4JYRwNHAeUFilWdUA69at49VXX+X+++9n8uTJAOTl5dG7d28GDRpEZmYmo0ePZsWKFSQSCUaNGlXNGUuSJEk6GLnSRkpddwGTgTkhhJuBlyg9PeoYSjco/iXQGLi6uhI8WD399NOcccYZtG7dmiOPPJL58+cDMGfOHBYuXEhGRgZFRUUsXLiQgoKCas5WkiRJ0sHKlTZSiooxPkbpHjMnAvcCy4B1lO4Z8xcgA/h9sp0q0aOPPspFF10EwEUXXcSjj5a+opWbm0tGRkZ1piZJkiSpBnGljZTCYoz/XwjhWWAo0Ak4DFgDvAk8EGN8rTrzOxh9/vnnvPjiiyxcuJAQAiUlJYQQ6NevHw0aNKju9CRJkiTVIBZtpBQXY3wdeL2686gpnnjiCQYPHsyf//znsljPnj2ZNWvWNu0aNWrE2rVrqzo9HQB2tnn2nm6CLUmSJG3l61FSCgkh1AshzAkhvBBCqLOLdnWTbV7fVTvtvUcffZT+/ftvExswYACTJk3aJnbUUUfRtWtX2rdv70bEkiRJkvYLV9pIqeVioDPwoxjjTo/yjjF+E0L4PTAt2eehqkmv6lX16oS8vLwdYldffTVXX73jfs/bF3IkSZIkqTK50kZKLecC78YYp+2uYYzx/yjdnPj8/Z6VJEmSJKnKWbSRUksnIG8v2s8EEvsnFUmSJElSdbJoI6WWo4FP9qL9J8BR+ykXSZIkSVI1smgjpZYNQMO9aN8Q2LifcpEkSZIkVSOLNlJq+QDI2Yv22cD7+ykXSZIkSVI1smgjpZY8oEsIIXt3DUMInYHvAS/t76QkSZIkSVXPoo2UWu4CIvB4COGUnTUKIbQBHgdKgHuqKLcaIy0tjUQiQfv27Tn//PNZv349RUVFtG/ffq/Geeihh/joo4/2U5Z7pmHDvXnbTpIkSVIqqV3dCUj6txjjOyGEm4AxwJshhCeAF4EPKS3mHA98HxgA1ANujDG+U03pVokJQ75XqeMNmTB7t23S09MpKCgA4OKLL2b8+PGce+65e/1dDz30EO3bt+fYY4/d677bKy4upnZt/8mWJEmSahJX2kgpJsZ4E/BLIACDgPuA54H/A/6SjNUCbogx3lxdedYU3bt3Z/ny5QCUlJQwbNgw2rVrR58+fdiwYQMABQUFdOnShQ4dOtC/f3++/PJLnnjiCfLz87n44otJJBJs2LCBF154gU6dOpGZmclll13Gpk2bAJg2bRpt2rShW7duXH311Zx11lkAjBkzhuHDh9OnTx8GDx5MUVER3bt3Jysri6ysLGbPLi1A5eXl0aNHD/r370/btm0ZMWIEW7ZsKZvDDTfcQMeOHenSpQuffPIJa9euJSMjg82bNwPw1Vdf0bx587J7SZIkSanBoo2UgmKMvwVaA7+mdM+aJcA7yeubgNYxxv+uvgxrhuLiYp5//nkyMzMBWLZsGVdccQVvv/02hx9+OE8++SQAgwcP5ne/+x0LFiwgMzOTsWPHct5555Gdnc0jjzxCQUEBIQQuvfRSpkyZwltvvUVxcTHjxo1j48aN/OxnP+P5559n1qxZrFq1apsc5s2bxzPPPMOkSZNo0qQJ06dPZ/78+UyZMoWrr766rN2cOXP4wx/+wFtvvcWKFSt46qmnAPj666/p0qULhYWF9OjRg/vuu49GjRrRq1cvnnvuOQAmT57MgAEDqFOnTlX8WiVJkiTtIYs2UoqKMb4XY/xVjPH0GGO7GGPb5PWYGON71Z3fwWzDhg0kEgmys7M54YQTGDp0KAAZGRkkEgkAOnfuTFFREWvWrGH16tX07NkTgCFDhjBz5swdxnznnXfIyMigdevW27RbsmQJLVq0ICMjA4CBAwdu0+/HP/4x6enpAGzevJlhw4aRmZnJ+eefz6JFi8ra5ebm0qJFC9LS0hg4cCCzZs0CoG7dumUrd7bmDHD55Zfz4IMPAvDggw/y05/+9Nv/4iRJkiRVKjdIkKTtlN/Tprx69eqVXaelpZW9HrUnYox7Fd+qQYMGZde33XYbTZs2pbCwkC1btlC/fv2yZyGEbfptva9Tp07ZdVpaGsXFxQB07dqVoqIiXn75ZUpKSvZ6k2VJkiRJ+58rbSTpWzjssMM44ogjeOWVVwCYOHFi2aqbRo0asXbtWgDatGlDUVFR2f44W9u1adOGd999t2wFzJQpU3b6XWvWrKFZs2bUqlWLiRMnUlJSUvZszpw5/POf/2TLli1MmTKFbt267Tb3wYMHM3DgQFfZSJIkSSnKoo0kfUsTJkxg1KhRdOjQgYKCAm688UYALr30UkaMGEEikSDGyIMPPsj5559PZmYmtWrVYsSIEaSnp3PPPfdwxhln0K1bN5o2bcphhx1W4feMHDmSCRMm0KVLF5YuXbrNKpzvfve7jB49mvbt25ORkUH//v13m/fFF1/Ml19+ucMrWZIkSZJSg69HSUppe3JEd2Vbt27dDrHmzZuzcOHCsvtf/OIXZdeJRILXX399hz4DBgxgwIABZfff//73efPNN3do17t3b5YsWUKMkSuuuILs7Gyg9PSo8lq1asWCBQvK7v/7v/+9F/UhhxxS4Sqd8nM577zzOO+888ruZ82axXnnncfhhx++Q79U1PvOrhXGX7rq1SrORJIkSaoaFm0kqZrdd999TJgwgW+++YZOnTrxs5/9bL9/51VXXcXzzz/PtGnT9vt3SZIkSdo3Fm0kqZpde+21XHvttfvcv1evXvTq1Wuv+tx55537/H2SJEmSqoZ72kiSJEmSJKUgizaSJEmSJEkpyKKNJEmSJElSCrJoI0mSJEmSlIIs2khSOZ9//jmJRIJEIsExxxzDcccdV3b/zTff7PE4v/zlL7n99tsrJaef/OQnPP3005UyliRJkqQDh6dHSUppU+ctr9Tx+nc+aZfPjzrqKAoKCgAYM2YMDRs25Be/+EWl5qADyxvPv1Vh/NQzM6s4E0mSJNU0rrSRaogQwgMhhE9DCAvLxcaEEP4VQihIfvqVe/afIYTlIYR3Qgh9y8XPSMaWhxBGl4tnhBDeCCEsCyFMCSHUrbrZVY0JEyaQm5tLIpFg5MiRbNmyBYDnnnuOrKwsOnbsSJ8+fcrav/XWW/Ts2ZMWLVpw9913A7B8+XLat2/P0KFDadeuHWeeeSYbN24EYP78+Zx66ql06NCBAQMGsGbNmh1ymD59OolEgszMTIYNG1a2+ufZZ5/l5JNPpnv37lx11VWcc845lJSUcNJJJ/HFF18AUFJSQosWLcruJUmSJKU2izZSzfEQcEYF8dtijInkZxpACKEtcBHQLtnnnhBCWgghDbgbOBNoCwxMtgX4XXKsVsCXwND9OpsqtnDhQqZOncrs2bMpKCiguLiYyZMn8/HHH/Pzn/+cqVOnUlhYyOTJk8v6LF26lOnTp/P6669z4403UlJSAsA777zDf/zHf/D222+Tnp5e9urTT37yE/7whz+wYMECTj75ZH79619vk8P69eu57LLLePLJJ3nrrbdYv3499957L+vXr2fkyJH84x//YObMmXz88ccApKWlMXDgQCZNmgTA3//+d3JycjjyyCOr4lcmSZIk6VuyaCPVEDHGmcCeLrE4G5gcY9wUY/wnsBzITX6WxxjfjTF+A0wGzg4hBOA04Ilk/wnAOZU6gWo2Y8YM5s6dS3Z2NolEgpdffpkVK1bw2muv0bt3b0488USAbQoiZ511FnXr1qVJkyYceeSRrFq1CoCTTjqJzMzSV2s6d+5MUVERn3/+ORs3bqRbt24ADBkyhJkzZ26Tw+LFi2nVqhUtW7YEYPDgwcycOZNFixZx8sknc+KJJxJCYODAgWV9hg4dyoQJEwB44IEH+OlPf7qffkOSJEmSKpt72ki6MoQwGMgH/l+M8UvgOOD1cm0+TMYAPtgufipwFLA6xlhcQftthBCGA8MBTjjhhMqaw34XY+Syyy7bYfXLU089RWnNakf16tUru05LS6O4uHin8RjjHuWwN3GA5s2bc8QRR/DSSy/x5ptvbvP6liRJkqTU5kobqWYbB7QEEsBK4A/JeEVViLgP8R2DMd4bY8yOMWY3btx47zOuJqeffjqPPfYYn332GVB6ytT7779P165defHFF3nvvfcA9nm/mKOPPpr09HRmz54NwMSJE+nZs+c2bdq2bcuyZct49913Afjf//1fevbsSbt27XjnnXf44IMPiDEyZcqUbfoNHTqUiy++mIsuuohatfxnX5IkSTpQuNJGqsFijJ9svQ4h3Af8LXn7IfCdck2PBz5KXlcU/ww4PIRQO7napnz7g0JmZia/+tWvOP3009myZQt16tRh/Pjx5OTkMG7cOM4++2xijBx77LE8//zz+/QdEydO5Oc//zkbNmzgpJNO4sEHH9zm+SGHHML999/PueeeS0lJCaeeeirDhg2jbt263HXXXZx++uk0btyYnJycbYpH/fv357LLLuPSSy/9Nr8CSZIkSVXMoo1Ug4UQmsUYVyZv+wNbT5Z6FpgUQvgjcCzQCphD6YqaViGEDOBflG5WPCjGGEMILwHnUbrPzRDgmcrIcXdHdO9PY8aM2eZ+0KBBDBo0aId2P/zhD/nhD3+4Tezmm2/e5n7JkiVl11uPFAcYPbrsAC6ysrJ44403dhj/f//3f8uu+/TpU+ErTqeffjrvvPMOMUZ+9rOfkZ2dXfZs/vz55Obm0qpVqx36SZIkSUpdFm2kGiKE8CjQCzg6hPAh8CugVwghQemrTEXAzwBijG+HEB4DFgHFwBUxxpLkOFcCfwfSgAdijG8nv+J6YHII4WbgTeD+KpqagHHjxvHII4+wadMmsrOzGTZsGAC/+c1vuPfee7c51UqSJEnSgcGijVRDxBgHVhDeaWElxvgb4DcVxKcB0yqIv0vp6VKqBqNGjWLUqFE7xG+44QZuuOGGashIkiRJ0rfljpSSJEmSJEkpyKKNJEmSJElSCrJoI0mSJEmSlIIs2kiSJEmSJKUgizaStJ20tDQSiQTt2rWjY8eO/PGPf2TLli3VnZYkSZKkGsbToySltM6jHq7U8eb9fvBu26Snp1NQUADAp59+yqBBg1izZg1jx46t1FwkSZIkaVdcaSNJu9CkSRPuvfde7rrrLmKMFBUV0b17d7KyssjKymL27NkA5OXl0atXL8477zzatGnDxRdfTIwRgLlz5/K9732Pjh07kpuby9q1aykpKWHUqFHk5OTQoUMH/vznP1fnNCVJkiSlIFfaSNJutGjRgi1btvDpp5/SpEkTpk+fTv369Vm2bBkDBw4kPz8fgDfffJO3336bY489lq5du/Lqq6+Sm5vLhRdeyJQpU8jJyeGrr74iPT2d+++/n8MOO4y5c+eyadMmunbtSp8+fcjIyKjm2UqSJElKFRZtJGkPbF01s3nzZq688koKCgpIS0tj6dKlZW1yc3M5/vjjAUgkEhQVFXHYYYfRrFkzcnJyADj00EMB+Mc//sGCBQt44oknAFizZg3Lli2zaCNJkiSpjEUbSdqNd999l7S0NJo0acLYsWNp2rQphYWFbNmyhfr165e1q1evXtl1WloaxcXFxBgJIewwZoyRO++8k759+1bJHCRJkiQdeNzTRpJ2YdWqVYwYMYIrr7ySEAJr1qyhWbNm1KpVi4kTJ1JSUrLL/m3atOGjjz5i7ty5AKxdu5bi4mL69u3LuHHj2Lx5MwBLly7l66+/3u/zkSRJknTgcKWNJG1nw4YNJBIJNm/eTO3atbnkkku47rrrABg5ciQDBgzg8ccfp3fv3jRo0GCXY9WtW5cpU6Zw1VVXsWHDBtLT05kxYwaXX345RUVFZGVlEWOkcePGPP3001UxPUmSJEkHCIs2klLanhzRXdl2tXqmVatWLFiwoOz+v//7vwHo1asXvXr1KovfddddZdc5OTm8/vrrO4z129/+lt/+9reVkLEkSZKkg5GvR0mSJEmSJKUgizaSJEmSJEkpyKKNJEmSJElSCrJoI0mSJEmSlIIs2kiSJEmSJKUgizaSJEmSJEkpyKKNJG2nYcOG1fK9RUVFtG/fHoD8/HyuvvrqaslDkiRJUmqoXd0JSNKu/O3teyp1vLPajazU8XampKSEtLS0fe6fnZ1NdnZ2JWYkSZIk6UDjShtJqkCMkVGjRtG+fXsyMzOZMmUKAHl5eZx11lll7a688koeeughAJo3b85NN91Et27dePzxx+nVqxfXX389ubm5tG7dmldeeQUoXVHTvXt3srKyyMrKYvbs2Tt8f/nvefnll0kkEiQSCTp16sTatWvJy8ujZ8+eXHDBBbRu3ZrRo0fzyCOPkJubS2ZmJitWrNjPvyFJkiRJ+5srbSSpAk899RQFBQUUFhby2WefkZOTQ48ePXbbr379+syaNQuA8ePHU1xczJw5c5g2bRpjx45lxowZNGnShOnTp1O/fn2WLVvGwIEDyc/P3+mYt956K3fffTddu3Zl3bp11K9fH4DCwkIWL17MkUceSYsWLbj88suZM2cOf/rTn7jzzju5/fbbK+eXIUmSJKlauNJGkiowa9YsBg4cSFpaGk2bNqVnz57MnTt3t/0uvPDCbe7PPfdcADp37kxRUREAmzdvZtiwYWRmZnL++eezaNGiXY7ZtWtXrrvuOu644w5Wr15N7dql9facnByaNWtGvXr1aNmyJX369AEgMzOz7LskSZIkHbgs2khSBWKMFcZr167Nli1byu43bty4zfMGDRpsc1+vXj0A0tLSKC4uBuC2226jadOmFBYWkp+fzzfffLPLXEaPHs1f/vIXNmzYQJcuXViyZMk2YwPUqlWr7L5WrVpl3yVJkiTpwGXRRpIq0KNHD6ZMmUJJSQmrVq1i5syZ5ObmcuKJJ7Jo0SI2bdrEmjVreOGFF/Z67DVr1tCsWTNq1arFxIkTKSkp2WX7FStWkJmZyfXXX092dnZZ0UaSJEnSwc09bSSpnOLiYurVq0f//v157bXX6NixIyEEbrnlFo455hgALrjgAjp06ECrVq3o1KnTXn/HyJEjGTBgAI8//ji9e/feYXXO9m6//XZeeukl0tLSaNu2LWeeeSavvfbaPs1PqiydRz1cYXze7wdXcSaSJEkHr7CzVwAkaX/Lzs6O22/Au3jxYk455ZRqyqh0c99hw4YxZ86casshVVTm32LqvOUVxm+evOPJWQBjL123Q+wPLz5SYduXrnq1wvjc7NwK4xm3nFVh/OjTbqww/sbzb1UYP/XMzArja9c+WmG8UaOBFcZ39rvp3/mkCuOpwqKNpJomhDAvxphd3XlIqll8PUqSksaPH8/AgQO5+eabqzsVSZIkSbJoI9UUIYQHQgifhhAWlosdGUKYHkJYlvx5RDIeQgh3hBCWhxAWhBCyyvUZkmy/LIQwpFy8cwjhrWSfO0IIoWpn+O2NGDGCRYsWlZ3CJEmSJEnVyaKNVHM8BJyxXWw08EKMsRXwQvIe4EygVfIzHBgHpUUe4FfAqUAu8KuthZ5km+Hl+m3/XZIkSZKkvWDRRqohYowzgS+2C58NTEheTwDOKRd/OJZ6HTg8hNAM6AtMjzF+EWP8EpgOnJF8dmiM8bVYulHWw+XG2pdc97WrKol/A0mSJKn6WbSRaramMcaVAMmfTZLx44APyrX7MBnbVfzDCuJ7rX79+nz++ecWDapRjJHPP/+c+vXrV3cqkiRJUo3mkd+SKlLRfjRxH+I7DhzCcEpfo+KEE07Y4fnxxx/Phx9+yKpVq/Y4WVW++vXrc/zxx1d3GpIkSVKNZtFGqtk+CSE0izGuTL7i9Gky/iHwnXLtjgc+SsZ7bRfPS8aPr6D9DmKM9wL3QumR39s/r1OnDhkZGfsyF0mSJEk6qPh6lFSzPQtsPQFqCPBMufjg5ClSXYA1yden/g70CSEckdyAuA/w9+SztSGELslTowaXG0uSJEmStA9caSPVECGERyldJXN0COFDSk+B+h/gsRDCUOB94Pxk82lAP2A5sB74KUCM8YsQwq+Bucl2N8UYt25u/HNKT6hKB55PfiRJkiRJ+8iijVRDxBgH7uTR9ytoG4ErdjLOA8ADFcTzgfbfJkdJkiRJ0r9tMPaOAAAVuklEQVT5epQkSZIkSVIKcqWNJEmqNH97+54K42e1G1nFmUiSJB34XGkjSZIkSZKUgizaSJIkSZIkpSCLNpIkSZIkSSnIoo0kSZIkSVIKsmgjSZIkSZKUgizaSJIkSZIkpSCLNpIkSZIkSSnIoo0kSZIkSVIKsmgjSZIkSZKUgizaSJIkSZIkpSCLNpIkSZIkSSnIoo0kSZIkSVIKql3dCUiSlAqKxmRU/ODUZ6s2EUmSJCnJlTaSJEmSJEkpyJU2kiTtgyv63VZh/H+mHFPFmUiSJOlgZdFGkqQqMGHI9yqMH3r1w1WciSRJkg4Uvh4lSZIkSZKUgizaSJIkSZIkpSCLNpIkSZIkSSnIoo0kSZIkSVIKsmgjSZIkSZKUgizaSJIkSZIkpSCLNpIkSZIkSSmodnUnIEnSt7H60UnVnYIkSZK0X7jSRpIkSZIkKQW50kaSJO3U1HnLqzsFSZKkGsuVNpIkSZIkSSnIoo0kSZIkSVIK8vUoSYQQioC1QAlQHGPMDiEcCUwBmgNFwAUxxi9DCAH4E9APWA9cGmOcnxxnCPDL5LA3xxgnVOU8JKmq7ez1sf6dT6riTCRJ0sHIlTaStuodY0zEGLOT96OBF2KMrYAXkvcAZwKtkp/hwDiAZJHnV8CpQC7wqxDCEVWYvyRJkiQdVCzaSNqZs4GtK2UmAOeUiz8cS70OHB5CaAb0BabHGL+IMX4JTAfOqOqkJUmSJOlgYdFGEkAE/hFCmBdCGJ6MNY0xrgRI/mySjB8HfFCu74fJ2M7i2wghDA8h5IcQ8letWlXJ05AkSZKkg4d72kgC6Bpj/CiE0ASYHkJYsou2oYJY3EV820CM9wL3AmRnZ+/wXJIkSZJUypU2kogxfpT8+SkwldI9aT5JvvZE8uenyeYfAt8p1/144KNdxCVJkiRJ+8CijVTDhRAahBAabb0G+gALgWeBIclmQ4BnktfPAoNDqS7AmuTrU38H+oQQjkhuQNwnGZMkSZIk7QNfj5LUFJhaepI3tYFJMcb/CyHMBR4LIQwF3gfOT7afRulx38spPfL7pwAxxi9CCL8G5ibb3RRj/KLqpiFJkiRJBxeLNlINF2N8F+hYQfxz4PsVxCNwxU7GegB4oLJzlKStPnvxpgrjR592YxVnIkmStP9ZtJF0QOs86uEK4/N+P7iKM5EkSZKkyuWeNpIkSZIkSSnIoo0kSZIkSVIK8vUoSZK03/W+s2uF8ZeuerWKM9m1tWsfrTDeqNHAKs5EkiTJoo0kSUpB/397dx5sWVXdcfz7o0GhBEWDCgFNoxjnKKFtSIgJosYWW4mJE5pEiKg4JzEomgRBzSBaahVqxAGnREREDMEBNdJRQbEbRAVR0wIWHVBCGBsZBFf+2OfpzeXc1++9PO59w/dTder03Wda55zX1X3X23vt9atW97bvfszaMUciSZI0OQ6PkiRJkiRJWoBM2kiSJEmSJC1AJm0kSZIkSZIWIGvaSFqSTrvgXb3tax/64jFHIkmSJElzY08bSZIkSZKkBcieNpIkLSL2IpMkSVo+7GkjSZIkSZK0ANnTRpIWoOuvP6G3fYcdDhpzJNLidvZnv9PbvvcTHz7mSCRJkmbPpI0kSQvQXod/uLf96IPHG4ckSZImx6SNpEXhlHM2TjoESZIkSRorkzaSNI31q1b3tj9qwzfGHIkkSZKk5cZCxJIkSZIkSQuQSRtJkiRJkqQFyKSNJEmSJEnSAmRNG0mStOy85IC39bb/44k7jzkSSZKk0UzaSJIkbcGHnvvbve13fXn/1OySJEnzwaSNpGXlMcfu29t+xsvOnNV5rvzS63vbd9r/yFnHJOn/75Kjdu/fsPep4w1EkiRpHpm0kaQxOPuz3+lt3/uJDx9zJJIkSZIWC5M2krTInXLOxt72p+61x5gjkSRJkjSfTNpIkiQtM3sd3l+L55w3/+mYI5EkSdMxaSNJE+QMNlrurjnho5MOQZIkacEyaSNJi0jfDDbOXiNJkiQtTSZtJAl/2y9pPE674F297Wsf+uIxR9JvoccnSdJyY9JGkuaR0w5LgtE1Y44+eLxxSJKkxW2rSQcgaelIsibJ95NsTHLEpOORJEmSpMXMnjaS5kWSFcA7gccDm4D1SU6tqu9ONrLla/Rv+jf3tjv8QZIkSVpYTNpImi+rgY1VdRFAko8BBwImbSRpQk45Z+OkQxib668/obd9hx0OGnMkkiTNH5M2kubLrsClA583AXtPKBZJ0jx6zLH79raf8bIze9vXr1rd2777MWt72zd/+QO3a/vJiFpgHz72i73t/3jizr3tfbPuweiZ95661x697RZpliRNQqpq0jFIWgKSPB14QlUd2n3+E2B1Vb1saL8XAC/oPj4Q+P5YAx2vnYArJx2E5sR3t7j5/hYv393ittTf369V1T0nHYSk5cWeNpLmyybgPgOfdwMuG96pqt4DvGdcQU1Skg1VtWrScWj2fHeLm+9v8fLdLW6+P0maf84eJWm+rAcekGT3JHcCngU4z7UkSZIkzZE9bSTNi6q6NclLgdOBFcDxVXXBhMOSJEmSpEXLpI2keVNVnwE+M+k4FpBlMQxsifLdLW6+v8XLd7e4+f4kaZ5ZiFiSJEmSJGkBsqaNJEmSJEnSAmTSRpLmWZI1Sb6fZGOSIyYdj2YuyfFJrkhy/qRj0ewkuU+SM5JcmOSCJK+YdEyauSTbJvlGkm917+/oScek2UmyIsk3k5w26VgkaSkxaSNJ8yjJCuCdwBOBhwAHJXnIZKPSLHwQWDPpIDQntwKvrKoHA/sAL/Hv3qJyM7B/VT0CeCSwJsk+E45Js/MK4MJJByFJS41JG0maX6uBjVV1UVXdAnwMOHDCMWmGqurLwFWTjkOzV1WXV9W53Z+vp3153HWyUWmmqtncfdymWyy8uEgk2Q14EvC+ScciSUuNSRtJml+7ApcOfN6EXxylsUqyEtgTOHuykWg2uuE15wFXAF+oKt/f4vF24FXAzycdiCQtNSZtJGl+pafN3xZLY5Jke+Bk4M+r6rpJx6OZq6rbquqRwG7A6iQPm3RM2rIka4ErquqcScciSUuRSRtJml+bgPsMfN4NuGxCsUjLSpJtaAmbf6mqT046Hs1NVV0DrMP6UovFvsBTklxCGxK8f5J/nmxIkrR0mLSRpPm1HnhAkt2T3Al4FnDqhGOSlrwkAd4PXFhVb510PJqdJPdMsmP35+2AxwHfm2xUmomqek1V7VZVK2n/5n2pqv54wmFJ0pJh0kaS5lFV3Qq8FDidVgj141V1wWSj0kwlOQH4GvDAJJuSPG/SMWnG9gX+hPZb/vO65YBJB6UZ2wU4I8m3acnvL1SVU0dLkpa9VFlqQZIkSZIkaaGxp40kSZIkSdICZNJGkiRJkiRpATJpI0mSJEmStACZtJEkSZIkSVqATNpIkiRJkiQtQCZtJElSrySVZN2k41jskmxIsnnScYxbkk90P0M7TToWSZIWK5M2kiQBSR6U5Ngk5ye5NsktSS5L8ukkz0uy7aRjXM6SHNUlAGa6XDLH62zfHX/aPN/CbGLYMHAfT59mvzcP7PeOccbYXf8t3bVXjfvakiQtF1tPOgBJkiYtyZHA62i/zPg68CFgM3BvYD/gfcCLgOX25fTBwE8nHURnXU/bI4EDgW8Bnxrads0dHdAY3Ao8HzhpeEOSbYDndvv4/zlJkpYo/5GXJC1rSV4LHA1cCjy9qs7u2Wct8MpxxzZpVfW9SccwparWMZS4SXIwLWlzXlUdNfag7ninAQcm2b2qLh7a9gfAPYFTgKeOPTJJkjQWDo+SJC1bSVYCRwE/Aw7oS9gAVNVpwJqhYw9OcnKSi5LcmOS6JGcm+eMR11rXDSXZJsmRSX6Y5KYk30vy/IH9Dkvyne6cm5IcnWSroXOt7M71wW5Y16eSXJXkhiRfTfL7Pde/W5LDk3ypO+8tSf47yalJ9hkRc29NmyS7JPlAkiu6OM9L8twk+3XHHDXi3rdO8tok/5nk5iSXJnlTkjv1XX++JFmR5OVJzu2e0eYkX0/yZ0P7vRS4vvv4pKHhVn/V7ZMkL0jyr0ku7u7/miT/Md1Qpjl6LxDgeT3bng9cS08vnIH7uUc3hGlj97z/pxvu9+iefddO3WeS1UlOTxsmuDnJF5PsNbT/lfwykbl+4Dn11e5Jklck+W73M395knckuUvPjqvSauH8qIv5irThYm8Z/ZgkSVq67GkjSVrODgG2AT5WVedPt2NV3TzU9E/Ad4EvA5cDvwIcAHwkyQOr6m9HnOpjwN7AZ2jJoqcB70nyM+A3aENeTgP+HXgKcCRtiNKbes61O/A14HzgOGAX4JnAZ5M8u6pOHNj3wcDfdfF+GrgauG93jScmeXJVfW66ZwCQ5F7AWcDK7lxnATsD7wI+v4XDPwo8GvgscB3teb0KuBftXcy7LuF1Mq1HzsW057QC+EPg/Un2qaoXdLt/A/gH4DXAf3bxTjmrW6/ozvEN4AzgJ7QeL2uBjyc5oqr63tVcnNsthyR5XVXd1t3TSuBxtGd+Y9+BA+/p/t36JNp7egawJsnBVfWRnkN/B3gjrVfTe4H70Xr1rEvysKr6UbffMV37b3X7Xda139JzzncCj6X93H0OeDzwEuDXgCcPxLw38BXgZuBU4EfAjsCvA68A/qrvXiVJWtKqysXFxcXFZVkutMRIAYfO4dj797TdqTvnz4Bdh7at6661HthxoP1+tC+6V9OSCrsObNsRuBL4b2DrgfaV3bkKePPQdVZ1178auOtA+92AnXpi3o32hfvCnm0FrBtqe3/X/qah9kfQvmwXcNSIez8HuMdA+12AjcBtwM5zeAcHd+f94DT7PL/b50xgu4H2u9KSXQU8ZaB9+67ttBHnC3C/nvbtaMmRG4FfGdq2Adg8i/va0MWwM3BY9+cnD2x/Y9f2CFripIB3DJ3jX7r2tw61P4yWBPwpcO+B9rUDP1NPGzrmlV37MUPtb+naV424j090238A7DL092TqHh8y0H5c1/bYnnPd7mfXxcXFxcVlOSwOj5IkLWe7dOtNsz2wqn7Y03YLrVfB1rSeBX2OqKprBo65CPgqLUHzhqr6r4Ft1wD/BuwE7NpzrmuB1w/FsIH2hX1HBmqdVNW1VXVlT8ybaF+uH5TkviNiBqAbxnRQd903Dp3nW8CHpzseeHVVXTVwzA1drFtxxxV5nhoCdXhV/aJXSlVdB/x19/HQmZ6smot62m8E3g1sC/ze3MO9nY8CN9CSTyRZQeuVtL575reTZHtaj5qraD21BuOc6pW1HfCcnsNPr6pPDLW9p1uvnuM9HFlVlw/EcAut2Peoc96u91Dfz64kScuBSRtJ0nKWbl2zPjC5b5J3djVpfjpV04M2FAf6kyzQehgMmxpack7Ptqkkzm49286tqut72td16z2HYt43yce7WjI3D8T8si3EPOWBtC/73x5x3a9u4fi+e7+0W999C8fO1Z7ATbRhZMO+NLDPjCW5f5Ljkvxg6N1PJSK29BxnrEsufRw4IMmv0nrE/CptSNIoD6clDtdXVV+Nmenu+3bvqHvX1zL3dzTT934C7e/i6UmOT/KcJLvP8ZqSJC0J1rSRJC1nlwEPoj8hMlKS+9FqmtydVoPj87QvtbfRhi49F7hz37FVdW1P863derpt2/Rs+8mIEH/cre82EPNTaT1qbgK+APyQ1oPj57RpzX9vVMwDps436rqj2oFf9BwaNnV/K7Zw7VlLsi3tni6pqtsl5qrq+iQ30HolzfScD6ENg9qelhybqs9zG632ykFs+TnO1ntpvWsOodWQ2UxLcIwy9Z4uH7F9qr3vvkdNlX4rc39HM3rvVbUuyf7Aq4Fn09U5SnIB8LdVdcocry9J0qJl0kaStJx9FdifNpTp/bM47i9phYcPqaoPDm5IchAtaTMO9x7RvnO3HkwCvYFWO2dVVV04uHOS45jZkJ7rtnDdUe0TUVU3JbmZEXF1w4juwi97M83Eq2hJkacPDyNKmwXsoDmGO1JVfS3J+bTivfcCjh/Rg2bK1HvfecT2XYb2WzCqm9q9S7g9ilas+iXAJ5I8uqrOmu54SZKWGodHSZKWsw/Qivb+UdeDYqQkg70n9ujWJ/fsOp/1TLbkN5Ps0NO+X7f+5kDbHsB3exI2W9FmDJqJ79HqjfzGiOvO9DzjdB6wXTcz0bD9u/W5A223detRvUr2oPVO+lTPtjvy3b+XlmxZwfRDowC+Q+vJ8qi+abWBx3Trc3u2zcaWntWcVdVNVfWVqnoNrefNVgzMNCVJ0nJh0kaStGxV1SXAUbTZbD6dpLcYbpI1tGEwUy7p1vsN7fcEZlHUdh7cjaFCs909PIfWi2JwOMklwAO6uihT+wZ4HTBtwmpKV0D2xO66fzN03UcAfzrrO7jjHd+tjxlMvHVJp6liyr/oZdUVFL6RNh16n0to/3969GBjkj+gDem5o3yAVlj6yVW1frodu144J9F6g/2fqee75OQLaff40dsfPSv/062nLWA9U0n2G5EMnOop9dP5uI4kSYuJw6MkSctaVf19kq1pyYv1Sc6im6KZ9mXxd4EH8H+Lqb6LVm/jpCQn04bXPAxYQysa+8wxhf9l4NCuF8mZtJ4Yz6QlFV7YFbGd8jba7Ebf7GL+GbAvLWHzb8y8F8MRtB4qr+que1Z33WcAn6FNQf3z/+d9zaf30e5tLXB+klNpPUP+ELgPbajRvw4d8+/A2u45TfVa+WJVfR04lvaMP53kJOAK2tTbj6MlSp5xR9xEVwy4r3fPKH8B7AO8Osm+tKGA96bFty3wvKr68TTHz8RUQeO3JVlNSxTeUlXHzPF8fwPsk+QM4GK6Xl3AE2jT3h8/zbGSJC1J9rSRJC17VfV6WtLlHbReJIcAhwNPohXsPZSBoT9V9W3aEJOzaDU3XgTclZYIePcYQ78Y+G3gauAw2hfyc4EDqurEwR2r6jjafV1Oq7nzHNoMPnszi2EyVfWT7pofBh5KSw7sCbyYNn03/LL2zcRV1c9pPVT+gpaIexFt+uwfd+u+nlGH0Yo2/y6tp8ob6N5/VZ0NPJ6WxDuQ1mvlzrSflY/cgbcyK917Wk1L1u0KvJL2HL4CPHa4FtMcr7GB9gyvps1A9gaGen7N0tuBT9KSpAfTatms7Nr3rKrZ1B6SJGlJSM9kCpIkaQFLspKWsPlQVR080WAGJPk74LXAmqo6fdLxSJIkLXb2tJEkSbMyWBdnoO3hwMuBq4D/GHtQkiRJS5A1bSRJ0mxtSLIROB+4gTac5Um0XwYdVlU3TTI4SZKkpcKkjSRJmq3jaAWHDwJ2AK4BTgfeUlXrJhiXJEnSkmJNG0mSJEmSpAXImjaSJEmSJEkLkEkbSZIkSZKkBcikjSRJkiRJ0gJk0kaSJEmSJGkBMmkjSZIkSZK0AJm0kSRJkiRJWoD+F3ehpax2rOIiAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2652423fb70>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Descriptions of Campaign Time x State\n",
"main_category Art Comics Crafts Dance Design Fashion Film & Video \\\n",
"run_time_months \n",
"0 2732 656 728 378 1472 1439 4270 \n",
"1 18750 7547 6101 2564 19483 15525 40282 \n",
"2 3927 1578 978 600 4353 2756 10859 \n",
"3 231 97 11 31 55 54 1090 \n",
"4 1 0 0 0 0 0 3 \n",
"\n",
"main_category Food Games Journalism Music Photography Publishing \\\n",
"run_time_months \n",
"0 1541 2277 370 3169 838 2498 \n",
"1 16592 22389 2946 32726 7113 26889 \n",
"2 3854 3786 783 9148 1621 5779 \n",
"3 67 69 49 752 117 244 \n",
"4 0 0 0 6 0 3 \n",
"\n",
"main_category Technology Theater \n",
"run_time_months \n",
"0 1327 1054 \n",
"1 20268 7263 \n",
"2 5383 1764 \n",
"3 68 158 \n",
"4 0 3 \n"
]
}
],
"source": [
"plt.figure(figsize = (10,6))\n",
"\n",
"ax = sns.countplot(x='run_time_months', hue='main_category', \n",
" data=ks_data[ks_data['run_time_months'] < 10], palette = 'Paired')\n",
"ax.set_title(\"Kickstarter Campaigns' Monthly Run Time by Success Status per Category\", fontsize=30)\n",
"ax.set_xlabel(\"Campaign Total Months\", fontsize=20)\n",
"ax.set_ylabel(\"Count\", fontsize=20)\n",
"plt.show()\n",
"\n",
"print(\"Descriptions of Campaign Time x State\")\n",
"print(pd.crosstab(ks_data[ks_data['run_time_months'] < 5]['run_time_months'], ks_data.main_category))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Comment\n",
"This is really interesting, it is confirmed that most campaign finish within a month, and that is also regardless of category. I now wonder how international are those kickstarter campaigns?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 6. How global is the Kickstarter community? How much has each country contributed? "
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['AUS', 'AUT', 'BEL', 'CAN', 'CHE', 'DEU', 'DNK', 'ESP', 'FRA', 'GBR', 'HKG', 'IRL', 'ITA', 'JPN', 'LUX', 'MEX', 'NLD', 'NOR', 'NZL', 'SGP', 'SWE', 'USA']\n"
]
}
],
"source": [
"#first to build a choropleth map, I have to adjust some country codes \n",
"#convert country code from 2 letter to 3 letter to map it out correctly\n",
"import pycountry\n",
"\n",
"input_countries = ['US', 'GB', 'CA','AU','DE','FR','IT','NL',\n",
" 'ES','SE','MX','NZ','DK','IE','CH','NO','HK', \n",
" 'BE','AT','SG','LU','JP']\n",
"\n",
"countries = {}\n",
"for country in pycountry.countries:\n",
" countries[country.alpha_2] = country.alpha_3\n",
"\n",
"codes = [countries.get(country, 'Unknown code') for country in input_countries]\n",
" \n",
"codes = sorted(codes)\n",
"print (codes) # prints ['AUS', 'AUT', 'BEL', etc..]"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Australia', 'Austria', 'Belgium', 'Canada', 'Denmark', 'France', 'Germany', 'Hong Kong', 'Ireland', 'Italy', 'Japan', 'Luxembourg', 'Mexico', 'Netherlands', 'New Zealand', 'Norway', 'Singapore', 'Spain', 'Sweden', 'Switzerland', 'United Kingdom', 'United States']\n"
]
}
],
"source": [
"# get country names to add to the legend\n",
"input_countries = ['US', 'GB', 'CA','AU','DE','FR','IT','NL',\n",
" 'ES','SE','MX','NZ','DK','IE','CH','NO','HK', \n",
" 'BE','AT','SG','LU','JP']\n",
"\n",
"countries = {}\n",
"for country in pycountry.countries:\n",
" countries[country.alpha_2] = country.name\n",
"\n",
"codes_names = [countries.get(country, 'Unknown code') for country in input_countries]\n",
"codes_names = sorted(codes_names)\n",
"print (codes_names)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"country\n",
"AT 9.152086e+06\n",
"AU 4.308827e+07\n",
"BE 4.083094e+06\n",
"CA 8.589252e+07\n",
"CH 1.239121e+07\n",
"DE 3.718813e+07\n",
"DK 7.135941e+06\n",
"ES 1.172830e+07\n",
"FR 3.283228e+07\n",
"GB 2.260742e+08\n",
"HK 9.158679e+06\n",
"IE 3.945442e+06\n",
"IT 1.387469e+07\n",
"JP 8.481102e+04\n",
"LU 4.515855e+05\n",
"MX 2.197721e+06\n",
"NL 2.825598e+07\n",
"NO 2.447907e+06\n",
"NZ 7.563028e+06\n",
"SE 1.235537e+07\n",
"SG 5.262985e+06\n",
"US 2.739565e+09\n",
"Name: usd_pledged_real, dtype: float64\n",
"country\n",
"AT 0.277780\n",
"AU 1.307794\n",
"BE 0.123928\n",
"CA 2.606967\n",
"CH 0.376092\n",
"DE 1.128716\n",
"DK 0.216587\n",
"ES 0.355972\n",
"FR 0.996509\n",
"GB 6.861692\n",
"HK 0.277980\n",
"IE 0.119750\n",
"IT 0.421118\n",
"JP 0.002574\n",
"LU 0.013706\n",
"MX 0.066704\n",
"NL 0.857612\n",
"NO 0.074298\n",
"NZ 0.229549\n",
"SE 0.375004\n",
"SG 0.159740\n",
"US 83.149929\n",
"Name: usd_pledged_real, dtype: float64\n"
]
}
],
"source": [
"#Figure the amount pledged per country \n",
"# this gives an ordered list by alphabet, so I had to adjust the codes and country codes to alhpabetical order to line up\n",
"total_pledged_by_country = ks_data.groupby(['country']).usd_pledged_real.sum()\n",
"\n",
"\n",
"country_percent_per_pledge = total_pledged_by_country/ks_data['usd_pledged_real'].sum() *100\n",
"\n",
"print(total_pledged_by_country)\n",
"\n",
"print(country_percent_per_pledge)"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"data": [
{
"autocolorscale": false,
"colorbar": {
"autotick": false,
"tickprefix": "%",
"title": "Pledges Percent in US$"
},
"colorscale": "Viridis",
"locations": [
"AUS",
"AUT",
"BEL",
"CAN",
"CHE",
"DEU",
"DNK",
"ESP",
"FRA",
"GBR",
"HKG",
"IRL",
"ITA",
"JPN",
"LUX",
"MEX",
"NLD",
"NOR",
"NZL",
"SGP",
"SWE",
"USA"
],
"marker": {
"line": {
"color": "rgb(180,180,180)",
"width": 0.5
}
},
"reversescale": true,
"text": [
"Australia",
"Austria",
"Belgium",
"Canada",
"Denmark",
"France",
"Germany",
"Hong Kong",
"Ireland",
"Italy",
"Japan",
"Luxembourg",
"Mexico",
"Netherlands",
"New Zealand",
"Norway",
"Singapore",
"Spain",
"Sweden",
"Switzerland",
"United Kingdom",
"United States"
],
"type": "choropleth",
"z": [
0.27777962857818767,
1.3077940722192756,
0.12392805602168806,
2.60696744316819,
0.37609182257575835,
1.1287157327451764,
0.2165865789584898,
0.3559716869158724,
0.9965092421280162,
6.861691982795656,
0.277979710273715,
0.11975010048686396,
0.42111780621172784,
0.0025741423785889178,
0.013706301967958138,
0.06670415721779795,
0.8576116300811663,
0.07429767239230554,
0.22954931662464056,
0.37500399234521076,
0.15973951932206126,
83.14992940459264
]
}
],
"layout": {
"geo": {
"projection": {
"type": "Mercator"
},
"showcoastlines": false,
"showframe": false
},
"title": "Most Generous Country on Kickstarter (percent of total pledges per country)"
}
},
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" type = 'choropleth',\n",
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" z = country_percent_per_pledge,\n",
" text = codes_names,\n",
" colorscale = 'Viridis',\n",
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},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Comment\n",
"It appears that the US donates about about 83% of the total amounts pledged on the website, what is that in dollar amount?"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
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"source": [
"data = [ dict(\n",
" type = 'choropleth',\n",
" locations = codes,\n",
" z = total_pledged_by_country,\n",
" text = codes_names,\n",
" #colorscale = [[0,\"rgb(5, 10, 172)\"],[0.35,\"rgb(40, 60, 190)\"],[0.5,\"rgb(70, 100, 245)\"],\\\n",
" # [0.6,\"rgb(90, 120, 245)\"],[0.7,\"rgb(106, 137, 247)\"],[1,\"rgb(220, 220, 220)\"]],\n",
" \n",
" colorscale = 'Viridis',\n",
" autocolorscale = False,\n",
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" ) ),\n",
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" tickprefix = '$',\n",
" title = 'pledges in Billions US$'),\n",
" ) ]\n",
"\n",
"layout = dict(\n",
" title = 'Most Generous Country on Kickstarter per total pledges per country',\n",
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" type = 'Mercator'\n",
" )\n",
" )\n",
")\n",
"\n",
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"py.iplot( fig, validate=False, filename='d3-world-map' )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Comment\n",
"Wow, that is almost 3 billion dollars. This is amazing, If I were to start a campaign, I'd focus my efforts on the US. However, we see that the website is not as international as we thought and that most of the campaigns happen in rich nations. This can either be a bummer or a completely new opportunity for people in different nations (more poor one) to explore!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 7. Given this historical data, Which campaign categories are more popular during which months? and which campaign categories raise more money during which months?"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Film Campaigns launched since 2009 :\n",
"3 5395\n",
"4 5221\n",
"6 5177\n",
"7 5160\n",
"5 5024\n",
"10 4776\n",
"2 4725\n",
"8 4616\n",
"11 4572\n",
"9 4313\n",
"1 4250\n",
"12 3275\n",
"Name: launched_month, dtype: int64\n",
"Music Campaigns launched since 2009 :\n",
"3 4233\n",
"5 4173\n",
"4 4109\n",
"7 4016\n",
"6 3992\n",
"2 3932\n",
"10 3827\n",
"11 3824\n",
"8 3791\n",
"1 3611\n",
"9 3598\n",
"12 2695\n",
"Name: launched_month, dtype: int64\n",
"Publishing Campaigns launched since 2009 :\n",
"10 3304\n",
"7 3266\n",
"5 3202\n",
"3 3133\n",
"11 3132\n",
"6 3037\n",
"8 3022\n",
"9 2987\n",
"4 2927\n",
"2 2736\n",
"1 2665\n",
"12 2002\n",
"Name: launched_month, dtype: int64\n",
"Games Campaigns launched since 2009 :\n",
"Series([], Name: launched_month, dtype: int64)\n",
"Technology Campaigns launched since 2009 :\n",
"7 2563\n",
"11 2494\n",
"10 2442\n",
"9 2397\n",
"8 2397\n",
"3 2369\n",
"6 2362\n",
"5 2276\n",
"4 2208\n",
"2 1990\n",
"1 1850\n",
"12 1698\n",
"Name: launched_month, dtype: int64\n",
"Design Campaigns launched since 2009 :\n",
"11 2706\n",
"10 2426\n",
"6 2281\n",
"9 2236\n",
"3 2222\n",
"7 2211\n",
"5 2123\n",
"8 2116\n",
"4 2087\n",
"2 1780\n",
"1 1752\n",
"12 1423\n",
"Name: launched_month, dtype: int64\n",
"Art Campaigns launched since 2009 :\n",
"7 2617\n",
"3 2420\n",
"5 2319\n",
"8 2303\n",
"6 2286\n",
"4 2186\n",
"10 2105\n",
"9 2080\n",
"2 2077\n",
"11 2020\n",
"1 1869\n",
"12 1359\n",
"Name: launched_month, dtype: int64\n",
"Food Campaigns launched since 2009 :\n",
"Series([], Name: launched_month, dtype: int64)\n",
"Fashion Campaigns launched since 2009 :\n",
"11 1911\n",
"10 1903\n",
"7 1881\n",
"6 1761\n",
"8 1697\n",
"3 1681\n",
"5 1648\n",
"9 1642\n",
"2 1537\n",
"4 1527\n",
"1 1455\n",
"12 1131\n",
"Name: launched_month, dtype: int64\n",
"Theater Campaigns launched since 2009 :\n",
"6 1138\n",
"5 1138\n",
"7 1056\n",
"4 1052\n",
"3 975\n",
"2 798\n",
"8 792\n",
"9 783\n",
"1 702\n",
"10 694\n",
"11 621\n",
"12 493\n",
"Name: launched_month, dtype: int64\n",
"Comics Campaigns launched since 2009 :\n",
"10 1023\n",
"3 947\n",
"5 911\n",
"7 898\n",
"8 888\n",
"6 871\n",
"9 861\n",
"4 833\n",
"11 816\n",
"2 786\n",
"1 687\n",
"12 357\n",
"Name: launched_month, dtype: int64\n",
"Photography Campaigns launched since 2009 :\n",
"7 1186\n",
"5 866\n",
"3 864\n",
"6 839\n",
"8 824\n",
"10 818\n",
"9 793\n",
"11 782\n",
"4 753\n",
"1 739\n",
"2 730\n",
"12 495\n",
"Name: launched_month, dtype: int64\n",
"Crafts Campaigns launched since 2009 :\n",
"7 851\n",
"8 789\n",
"10 729\n",
"3 717\n",
"11 696\n",
"9 646\n",
"1 639\n",
"2 614\n",
"4 586\n",
"6 584\n",
"5 556\n",
"12 411\n",
"Name: launched_month, dtype: int64\n",
"Journalism Campaigns launched since 2009 :\n",
"7 470\n",
"8 379\n",
"3 370\n",
"9 354\n",
"6 350\n",
"11 341\n",
"4 341\n",
"2 336\n",
"5 319\n",
"1 314\n",
"10 306\n",
"12 268\n",
"Name: launched_month, dtype: int64\n",
"Dance Campaigns launched since 2009 :\n",
"4 362\n",
"3 339\n",
"5 338\n",
"7 332\n",
"6 309\n",
"10 305\n",
"2 294\n",
"8 284\n",
"9 275\n",
"11 271\n",
"1 251\n",
"12 213\n",
"Name: launched_month, dtype: int64\n"
]
}
],
"source": [
"main_catigs = ks_data[\"main_category\"].value_counts()\n",
"main_catigs_Film = ks_data[ks_data[\"main_category\"] == \"Film & Video\"][\"launched_month\"].value_counts()\n",
"main_catigs_Music = ks_data[ks_data[\"main_category\"] == \"Music\"][\"launched_month\"].value_counts()\n",
"main_catigs_Publishing = ks_data[ks_data[\"main_category\"] == \"Publishing\"][\"launched_month\"].value_counts()\n",
"main_catigs_Games = ks_data[ks_data[\"main_category\"] == \"Games \"][\"launched_month\"].value_counts()\n",
"main_catigs_Technology = ks_data[ks_data[\"main_category\"] == \"Technology\"][\"launched_month\"].value_counts()\n",
"main_catigs_Design = ks_data[ks_data[\"main_category\"] == \"Design\"][\"launched_month\"].value_counts()\n",
"main_catigs_Art = ks_data[ks_data[\"main_category\"] == \"Art\"][\"launched_month\"].value_counts()\n",
"main_catigs_Food = ks_data[ks_data[\"main_category\"] == \"Food \"][\"launched_month\"].value_counts()\n",
"main_catigs_Fashion = ks_data[ks_data[\"main_category\"] == \"Fashion\"][\"launched_month\"].value_counts()\n",
"main_catigs_Theater = ks_data[ks_data[\"main_category\"] == \"Theater\"][\"launched_month\"].value_counts()\n",
"main_catigs_Comics = ks_data[ks_data[\"main_category\"] == \"Comics\"][\"launched_month\"].value_counts()\n",
"main_catigs_Photography = ks_data[ks_data[\"main_category\"] == \"Photography\"][\"launched_month\"].value_counts()\n",
"main_catigs_Crafts = ks_data[ks_data[\"main_category\"] == \"Crafts\"][\"launched_month\"].value_counts()\n",
"main_catigs_Journalism = ks_data[ks_data[\"main_category\"] == \"Journalism\"][\"launched_month\"].value_counts()\n",
"main_catigs_Dance = ks_data[ks_data[\"main_category\"] == \"Dance\"][\"launched_month\"].value_counts()\n",
"\n",
"\n",
" \n",
"print('Film Campaigns launched since 2009 :')\n",
"print (main_catigs_Film)\n",
"\n",
"print('Music Campaigns launched since 2009 :')\n",
"print (main_catigs_Music)\n",
"\n",
"print('Publishing Campaigns launched since 2009 :')\n",
"print (main_catigs_Publishing)\n",
"\n",
"print('Games Campaigns launched since 2009 :')\n",
"print (main_catigs_Games )\n",
"\n",
"print('Technology Campaigns launched since 2009 :')\n",
"print (main_catigs_Technology)\n",
"\n",
"print('Design Campaigns launched since 2009 :')\n",
"print (main_catigs_Design)\n",
"\n",
"print('Art Campaigns launched since 2009 :')\n",
"print (main_catigs_Art)\n",
"\n",
"print('Food Campaigns launched since 2009 :')\n",
"print (main_catigs_Food )\n",
"\n",
"print('Fashion Campaigns launched since 2009 :')\n",
"print (main_catigs_Fashion)\n",
"\n",
"print('Theater Campaigns launched since 2009 :')\n",
"print (main_catigs_Theater)\n",
"\n",
"print('Comics Campaigns launched since 2009 :')\n",
"print (main_catigs_Comics)\n",
"\n",
"print('Photography Campaigns launched since 2009 :')\n",
"print (main_catigs_Photography)\n",
"\n",
"print('Crafts Campaigns launched since 2009 :')\n",
"print (main_catigs_Crafts)\n",
"\n",
"print('Journalism Campaigns launched since 2009 :')\n",
"print (main_catigs_Journalism)\n",
"\n",
"print('Dance Campaigns launched since 2009 :')\n",
"print (main_catigs_Dance)\n"
]
},
{
"cell_type": "code",
"execution_count": 84,
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"###\n",
"#Plot the total number of campaign per month sinice 2012, to show what month is most popular per campaign\n",
"### \n",
"\n",
"trace_0 = go.Scatter(x= main_catigs_Film.values,\n",
" y= main_catigs_Film.index,\n",
" name='Film Campaigns launched since 2009 Per Month')\n",
"\n",
"trace_1 = go.Scatter(x= main_catigs_Music.values,\n",
" y= main_catigs_Music.index,\n",
" name='Music Campaigns launched since 2009 Per Month')\n",
"\n",
"\n",
"trace_2 = go.Scatter(x= main_catigs_Publishing.values,\n",
" y= main_catigs_Publishing.index,\n",
" name='Publishing Campaigns launched since 2009 Per Month')\n",
"\n",
"trace_3 = go.Scatter(x= main_catigs_Games.values,\n",
" y= main_catigs_Games.index,\n",
" name='Games Campaigns launched since 2009 Per Month')\n",
"\n",
"trace_4 = go.Scatter(x= main_catigs_Technology.values,\n",
" y= main_catigs_Technology.index,\n",
" name='Technology Campaigns launched since 2009 Per Month')\n",
"\n",
"trace_5 = go.Scatter(x= main_catigs_Design.values,\n",
" y= main_catigs_Design.index,\n",
" name='Design Campaigns launched since 2009 Per Month')\n",
"\n",
"trace_6 = go.Scatter(x= main_catigs_Art.values,\n",
" y= main_catigs_Art.index,\n",
" name='Art Campaigns launched since 2009 Per Month')\n",
"\n",
"trace_7 = go.Scatter(x= main_catigs_Food.values,\n",
" y= main_catigs_Food.index,\n",
" name='Food Campaigns launched since 2009 Per Month')\n",
"\n",
"trace_8 = go.Scatter(x= main_catigs_Fashion.values,\n",
" y= main_catigs_Fashion.index,\n",
" name='Fashion Campaigns launched since 2009 Per Month')\n",
"\n",
"trace_9 = go.Scatter(x= main_catigs_Theater.values,\n",
" y= main_catigs_Theater.index,\n",
" name='Theater Campaigns launched since 2009 Per Month')\n",
"\n",
"trace_10 = go.Scatter(x= main_catigs_Comics.values,\n",
" y= main_catigs_Comics.index,\n",
" name='Comics Campaigns launched since 2009 Per Month')\n",
"\n",
"trace_11 = go.Scatter(x= main_catigs_Photography.values,\n",
" y= main_catigs_Photography.index,\n",
" name='Photography Campaigns launched since 2009 Per Month')\n",
"\n",
"trace_12 = go.Scatter(x= main_catigs_Crafts.values,\n",
" y= main_catigs_Crafts.index,\n",
" name='Crafts Campaigns launched since 2009 Per Month')\n",
"\n",
"trace_13 = go.Scatter(x= main_catigs_Journalism.values,\n",
" y= main_catigs_Journalism.index,\n",
" name='Journalism Campaigns launched since 2009 Per Month')\n",
"\n",
"trace_14 = go.Scatter(x= main_catigs_Dance.values,\n",
" y= main_catigs_Dance.index,\n",
" name='Dance Campaigns launched since 2009 Per Month')\n",
"\n",
"data = [trace_0, trace_1, trace_2,trace_3,trace_4,trace_5,trace_6,trace_7,trace_8,trace_9,\n",
" trace_10,trace_11,trace_12,trace_13,trace_14]\n",
"\n",
"\n",
"\n",
"updatemenus = list([\n",
" dict(active=-1,\n",
" buttons=list([ \n",
" \n",
" dict(label = 'All Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [True, True, True, True,\n",
" True, True, True,True,\n",
" True, True, True,True,\n",
" True, True, True]},\n",
" {'title': 'All Campaigns launched since 2009 Per Month'}]),\n",
" \n",
" dict(label = 'Film Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [True, False, False, False,\n",
" False, False, False,False,\n",
" False, False, False,False,\n",
" False, False, False]},\n",
" {'title': 'Film Campaigns launched since 2009 Per Month'}]),\n",
" \n",
" dict(label = 'Music Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [False, True, False, False,\n",
" False, False, False,False,\n",
" False, False, False,False,\n",
" False, False, False]},\n",
" {'title': 'Music Campaigns launched since 2009 Per Month'}]),\n",
" \n",
" dict(label = 'Publishing Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [False, False, True, False,\n",
" False, False, False,False,\n",
" False, False, False,False,\n",
" False, False, False]},\n",
" {'title': 'PublishingCampaigns launched since 2009 Per Month'}]),\n",
" \n",
" dict(label = 'Games Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [False, False, False,True, \n",
" False, False, False,False,\n",
" False, False, False,False,\n",
" False, False, False]},\n",
" {'title': 'Games Campaigns launched since 2009 Per Month'}]),\n",
" \n",
" dict(label = 'Technology Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [False, False, False, False,\n",
" True, False, False,False,\n",
" False, False, False,False,\n",
" False, False, False]},\n",
" {'title': 'Technology Campaigns launched since 2009 Per Month'}]),\n",
" \n",
" dict(label = 'Design Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [False, False, False, False,\n",
" False, True, False,False,\n",
" False, False, False,False,\n",
" False, False, False]},\n",
" {'title': 'Design Campaigns launched since 2009 Per Month'}]),\n",
"\n",
" dict(label = 'Art Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [False, False, False, False,\n",
" False, False, True, False,\n",
" False, False, False,False,\n",
" False, False, False]},\n",
" {'title': 'Art Campaigns launched since 2009 Per Month'}]),\n",
" \n",
" dict(label = 'Food Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [False, False, False, False,\n",
" False, False, False, True, \n",
" False, False, False,False,\n",
" False, False, False]},\n",
" {'title': 'Food Campaigns launched since 2009 Per Month'}]),\n",
" \n",
" dict(label = 'Fashion Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [False, False, False, False, \n",
" False, False, False,False,\n",
" True, False, False,False,\n",
" False, False, False]},\n",
" {'title': 'Fashion Campaigns launched since 2009 Per Month'}]),\n",
" \n",
" dict(label = 'Theater Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [False, False, False, False, \n",
" False, False, False,False,\n",
" False, True, False,False,\n",
" False, False, False]},\n",
" {'title': 'Theater Campaigns launched since 2009 Per Month'}]),\n",
" \n",
" dict(label = 'Comics Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [False, False, False, False, \n",
" False, False, False,False,\n",
" False, False, True, False,\n",
" False, False, False]},\n",
" {'title': 'Comics Campaigns launched since 2009 Per Month'}]),\n",
" \n",
" dict(label = 'Photography Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [False, False, False, False, \n",
" False, False, False,False,\n",
" False, False, False, True, \n",
" False, False, False]},\n",
" {'title': 'Photography Campaigns launched since 2009 Per Month'}]),\n",
" \n",
" dict(label = 'Crafts Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [False, False, False, False, \n",
" False, False, False,False,\n",
" False, False, False,False,\n",
" True, False, False]},\n",
" {'title': 'Crafts Campaigns launched since 2009 Per Month'}]),\n",
" \n",
" dict(label = 'Journalism Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [False, False, False, False, \n",
" False, False, False,False,\n",
" False, False, False,False,\n",
" False, True, False]},\n",
" {'title': 'Journalism Campaigns launched since 2009 Per Month'}]),\n",
" \n",
" dict(label = 'Dance Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [False, False, False, False, \n",
" False, False, False,False,\n",
" False, False, False,False,\n",
" False, False, True]},\n",
" {'title': 'Dance Campaigns launched since 2009 Per Month'}])\n",
" ]),\n",
" ),\n",
" dict (direction = 'down',\n",
" pad = {'r': 10, 't': 10},\n",
" showactive = True,\n",
" x = 0.5,\n",
" xanchor = 'center',\n",
" y = 1.12,\n",
" yanchor = 'top')\n",
"])\n",
"\n",
"\n",
"\n",
"\n",
"layout = go.Layout(\n",
" dict(title= 'Campaigns\\' Most popular Months', \n",
" showlegend=False,\n",
" updatemenus=updatemenus),\n",
" xaxis=dict(\n",
" title='Total Number of Campaigns in that Month since 2009',\n",
" titlefont=dict(\n",
" family='Ariel',\n",
" size=14,\n",
" color='#7f7f7f'\n",
" )\n",
" ),\n",
" yaxis=dict(\n",
" title='Month of the Year',\n",
" titlefont=dict(\n",
" \n",
" family='Ariel',\n",
" size=14,\n",
" color='#7f7f7f'\n",
" \n",
" )\n",
" )\n",
")\n",
"fig = dict(data=data, layout=layout)\n",
"py.iplot(fig, filename='update_dropdown')\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Comment\n",
"Very interesting to see which months have more campaigns, and in which months are different campaign categories more popular. I wonder also about the amount pledged per campaign. can we see the average amounts pledged per campaign category since 2009?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Film Campaigns launched since 2009 :\n",
"launched_month\n",
"1 5530.966466\n",
"2 7021.610665\n",
"3 7267.626966\n",
"4 7569.778209\n",
"5 6873.355382\n",
"6 6229.379090\n",
"7 6922.461432\n",
"8 5542.201367\n",
"9 6718.071111\n",
"10 7218.699971\n",
"11 7660.090731\n",
"12 5358.045933\n",
"Name: usd_pledged_real, dtype: float64\n",
"Music Campaigns launched since 2009 :\n",
"launched_month\n",
"1 3826.423304\n",
"2 4204.612325\n",
"3 4286.393728\n",
"4 4197.425785\n",
"5 3968.113106\n",
"6 4208.711423\n",
"7 3690.679898\n",
"8 3970.968077\n",
"9 4490.312001\n",
"10 4735.866564\n",
"11 4706.848146\n",
"12 3534.422523\n",
"Name: usd_pledged_real, dtype: float64\n",
"Publishing Campaigns launched since 2009 :\n",
"launched_month\n",
"1 3090.935002\n",
"2 3310.110205\n",
"3 3782.078120\n",
"4 4052.116187\n",
"5 3833.281018\n",
"6 4195.356345\n",
"7 3215.981996\n",
"8 3376.996294\n",
"9 4077.408875\n",
"10 4250.118354\n",
"11 4041.049658\n",
"12 2574.218472\n",
"Name: usd_pledged_real, dtype: float64\n",
"Games Campaigns launched since 2009 :\n",
"Series([], Name: usd_pledged_real, dtype: float64)\n",
"Technology Campaigns launched since 2009 :\n",
"launched_month\n",
"1 19468.770719\n",
"2 21392.395457\n",
"3 31923.817011\n",
"4 20202.250050\n",
"5 30719.120492\n",
"6 22910.975140\n",
"7 24146.078915\n",
"8 17530.139307\n",
"9 27963.586600\n",
"10 23432.666974\n",
"11 28019.486387\n",
"12 14672.639576\n",
"Name: usd_pledged_real, dtype: float64\n",
"Design Campaigns launched since 2009 :\n",
"launched_month\n",
"1 22276.745126\n",
"2 34726.789399\n",
"3 20636.580387\n",
"4 30102.314427\n",
"5 33105.750217\n",
"6 25597.308619\n",
"7 42290.610647\n",
"8 25326.508398\n",
"9 24347.668520\n",
"10 28727.776533\n",
"11 25119.768186\n",
"12 19556.996283\n",
"Name: usd_pledged_real, dtype: float64\n",
"Art Campaigns launched since 2009 :\n",
"launched_month\n",
"1 2973.418331\n",
"2 3866.186394\n",
"3 3423.542760\n",
"4 3607.347763\n",
"5 3072.068909\n",
"6 3819.303815\n",
"7 2829.927321\n",
"8 3306.587959\n",
"9 4909.813740\n",
"10 3797.759515\n",
"11 3425.071500\n",
"12 2442.523319\n",
"Name: usd_pledged_real, dtype: float64\n",
"Food Campaigns launched since 2009 :\n",
"Series([], Name: usd_pledged_real, dtype: float64)\n",
"Fashion Campaigns launched since 2009 :\n",
"launched_month\n",
"1 4562.206151\n",
"2 6259.831399\n",
"3 7028.050886\n",
"4 6747.502246\n",
"5 7503.553459\n",
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],
"source": [
"main_catigs = ks_data[\"main_category\"].value_counts()\n",
"main_catigs_m_Film = ks_data[ks_data[\"main_category\"] == \"Film & Video\"].groupby(['launched_month']).usd_pledged_real.mean()\n",
"main_catigs_m_Music = ks_data[ks_data[\"main_category\"] == \"Music\"].groupby(['launched_month']).usd_pledged_real.mean()\n",
"main_catigs_m_Publishing = ks_data[ks_data[\"main_category\"] == \"Publishing\"].groupby(['launched_month']).usd_pledged_real.mean()\n",
"main_catigs_m_Games = ks_data[ks_data[\"main_category\"] == \"Games \"].groupby(['launched_month']).usd_pledged_real.mean()\n",
"main_catigs_m_Technology = ks_data[ks_data[\"main_category\"] == \"Technology\"].groupby(['launched_month']).usd_pledged_real.mean()\n",
"main_catigs_m_Design = ks_data[ks_data[\"main_category\"] == \"Design\"].groupby(['launched_month']).usd_pledged_real.mean()\n",
"main_catigs_m_Art = ks_data[ks_data[\"main_category\"] == \"Art\"].groupby(['launched_month']).usd_pledged_real.mean()\n",
"main_catigs_m_Food = ks_data[ks_data[\"main_category\"] == \"Food \"].groupby(['launched_month']).usd_pledged_real.mean()\n",
"main_catigs_m_Fashion = ks_data[ks_data[\"main_category\"] == \"Fashion\"].groupby(['launched_month']).usd_pledged_real.mean()\n",
"main_catigs_m_Theater = ks_data[ks_data[\"main_category\"] == \"Theater\"].groupby(['launched_month']).usd_pledged_real.mean()\n",
"main_catigs_m_Comics = ks_data[ks_data[\"main_category\"] == \"Comics\"].groupby(['launched_month']).usd_pledged_real.mean()\n",
"main_catigs_m_Photography = ks_data[ks_data[\"main_category\"] == \"Photography\"].groupby(['launched_month']).usd_pledged_real.mean()\n",
"main_catigs_m_Crafts = ks_data[ks_data[\"main_category\"] == \"Crafts\"].groupby(['launched_month']).usd_pledged_real.mean()\n",
"main_catigs_m_Journalism = ks_data[ks_data[\"main_category\"] == \"Journalism\"].groupby(['launched_month']).usd_pledged_real.mean()\n",
"main_catigs_m_Dance = ks_data[ks_data[\"main_category\"] == \"Dance\"].groupby(['launched_month']).usd_pledged_real.mean()\n",
"\n",
"\n",
" \n",
"print('Film Campaigns launched since 2009 :')\n",
"print (main_catigs_m_Film)\n",
"\n",
"print('Music Campaigns launched since 2009 :')\n",
"print (main_catigs_m_Music)\n",
"\n",
"print('Publishing Campaigns launched since 2009 :')\n",
"print (main_catigs_m_Publishing)\n",
"\n",
"print('Games Campaigns launched since 2009 :')\n",
"print (main_catigs_m_Games )\n",
"\n",
"print('Technology Campaigns launched since 2009 :')\n",
"print (main_catigs_m_Technology)\n",
"\n",
"print('Design Campaigns launched since 2009 :')\n",
"print (main_catigs_m_Design)\n",
"\n",
"print('Art Campaigns launched since 2009 :')\n",
"print (main_catigs_m_Art)\n",
"\n",
"print('Food Campaigns launched since 2009 :')\n",
"print (main_catigs_m_Food )\n",
"\n",
"print('Fashion Campaigns launched since 2009 :')\n",
"print (main_catigs_m_Fashion)\n",
"\n",
"print('Theater Campaigns launched since 2009 :')\n",
"print (main_catigs_m_Theater)\n",
"\n",
"print('Comics Campaigns launched since 2009 :')\n",
"print (main_catigs_m_Comics)\n",
"\n",
"print('Photography Campaigns launched since 2009 :')\n",
"print (main_catigs_m_Photography)\n",
"\n",
"print('Crafts Campaigns launched since 2009 :')\n",
"print (main_catigs_m_Crafts)\n",
"\n",
"print('Journalism Campaigns launched since 2009 :')\n",
"print (main_catigs_m_Journalism)\n",
"\n",
"print('Dance Campaigns launched since 2009 :')\n",
"print (main_catigs_m_Dance)\n"
]
},
{
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"execution_count": null,
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{
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\"update\"}, {\"args\": [{\"visible\": [false, true, false, false, false, false, false, false, false, false, false, false, false, false, false]}, {\"title\": \"Average Amount raised for Music Campaigns launched since 2009 Per Month\"}], \"label\": \"Music Campaigns launched since 2009 Per Month\", \"method\": \"update\"}, {\"args\": [{\"visible\": [false, false, true, false, false, false, false, false, false, false, false, false, false, false, false]}, {\"title\": \"Average Amount raised for PublishingCampaigns launched since 2009 Per Month\"}], \"label\": \"Publishing Campaigns launched since 2009 Per Month\", \"method\": \"update\"}, {\"args\": [{\"visible\": [false, false, false, true, false, false, false, false, false, false, false, false, false, false, false]}, {\"title\": \"Average Amount raised for Games Campaigns launched since 2009 Per Month\"}], \"label\": \"Games Campaigns launched since 2009 Per Month\", \"method\": \"update\"}, {\"args\": [{\"visible\": [false, false, false, false, true, false, false, false, false, false, false, false, false, false, false]}, {\"title\": \"Average Amount raised for Technology Campaigns launched since 2009 Per Month\"}], \"label\": \"Technology Campaigns launched since 2009 Per Month\", \"method\": \"update\"}, {\"args\": [{\"visible\": [false, false, false, false, false, true, false, false, false, false, false, false, false, false, false]}, {\"title\": \"Average Amount raised for Design Campaigns launched since 2009 Per Month\"}], \"label\": \"Design Campaigns launched since 2009 Per Month\", \"method\": \"update\"}, {\"args\": [{\"visible\": [false, false, false, false, false, false, true, false, false, false, false, false, false, false, false]}, {\"title\": \"Average Amount raised for Art Campaigns launched since 2009 Per Month\"}], \"label\": \"Art Campaigns launched since 2009 Per Month\", \"method\": \"update\"}, {\"args\": [{\"visible\": [false, false, false, false, false, false, false, true, false, false, false, false, false, false, false]}, {\"title\": \"Average Amount raised for Food Campaigns launched since 2009 Per Month\"}], \"label\": \"Food Campaigns launched since 2009 Per Month\", \"method\": \"update\"}, {\"args\": [{\"visible\": [false, false, false, false, false, false, false, false, true, false, false, false, false, false, false]}, {\"title\": \"Average Amount raised for Fashion Campaigns launched since 2009 Per Month\"}], \"label\": \"Fashion Campaigns launched since 2009 Per Month\", \"method\": \"update\"}, {\"args\": [{\"visible\": [false, false, false, false, false, false, false, false, false, true, false, false, false, false, false]}, {\"title\": \"Average Amount raised for Theater Campaigns launched since 2009 Per Month\"}], \"label\": \"Theater Campaigns launched since 2009 Per Month\", \"method\": \"update\"}, {\"args\": [{\"visible\": [false, false, false, false, false, false, false, false, false, false, true, false, false, false, false]}, {\"title\": \"Average Amount raised for Comics Campaigns launched since 2009 Per Month\"}], \"label\": \"Comics Campaigns launched since 2009 Per Month\", \"method\": \"update\"}, {\"args\": [{\"visible\": [false, false, false, false, false, false, false, false, false, false, false, true, false, false, false]}, {\"title\": \"Average Amount raised for Photography Campaigns launched since 2009 Per Month\"}], \"label\": \"Photography Campaigns launched since 2009 Per Month\", \"method\": \"update\"}, {\"args\": [{\"visible\": [false, false, false, false, false, false, false, false, false, false, false, false, true, false, false]}, {\"title\": \"Average Amount raised for Crafts Campaigns launched since 2009 Per Month\"}], \"label\": \"Crafts Campaigns launched since 2009 Per Month\", \"method\": \"update\"}, {\"args\": [{\"visible\": [false, false, false, false, false, false, false, false, false, false, false, false, false, true, false]}, {\"title\": \"Average Amount raised for Journalism Campaigns launched since 2009 Per Month\"}], \"label\": \"Journalism Campaigns launched since 2009 Per Month\", \"method\": \"update\"}, {\"args\": [{\"visible\": [false, false, false, false, false, false, false, false, false, false, false, false, false, false, true]}, {\"title\": \"Average Amount raised for Dance Campaigns launched since 2009 Per Month\"}], \"label\": \"Dance Campaigns launched since 2009 Per Month\", \"method\": \"update\"}]}, {\"direction\": \"down\", \"pad\": {\"r\": 10, \"t\": 10}, \"showactive\": true, \"x\": 0.5, \"xanchor\": \"center\", \"y\": 1.12, \"yanchor\": \"top\"}], \"xaxis\": {\"title\": \"Average Amount raised\", \"titlefont\": {\"color\": \"#7f7f7f\", \"family\": \"Ariel\", \"size\": 14}}, \"yaxis\": {\"title\": \"Month of the Year\", \"titlefont\": {\"color\": \"#7f7f7f\", \"family\": \"Ariel\", \"size\": 14}}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"###\n",
"#Plot the average amount raised per campaign category per month sinice 2012, to show what month is most popular per campaign\n",
"### \n",
"trace_0 = go.Scatter(x= main_catigs_m_Film.values,\n",
" y= main_catigs_m_Film.index,\n",
" name='Film Campaigns launched since 2009 Per Month')\n",
"\n",
"trace_1 = go.Scatter(x= main_catigs_m_Music.values,\n",
" y= main_catigs_m_Music.index,\n",
" name='Music Campaigns launched since 2009 Per Month')\n",
"\n",
"\n",
"trace_2 = go.Scatter(x= main_catigs_m_Publishing.values,\n",
" y= main_catigs_m_Publishing.index,\n",
" name='Publishing Campaigns launched since 2009 Per Month')\n",
"\n",
"trace_3 = go.Scatter(x= main_catigs_m_Games.values,\n",
" y= main_catigs_m_Games.index,\n",
" name='Games Campaigns launched since 2009 Per Month')\n",
"\n",
"trace_4 = go.Scatter(x= main_catigs_m_Technology.values,\n",
" y= main_catigs_m_Technology.index,\n",
" name='Technology Campaigns launched since 2009 Per Month')\n",
"\n",
"trace_5 = go.Scatter(x= main_catigs_m_Design.values,\n",
" y= main_catigs_m_Design.index,\n",
" name='Design Campaigns launched since 2009 Per Month')\n",
"\n",
"trace_6 = go.Scatter(x= main_catigs_m_Art.values,\n",
" y= main_catigs_m_Art.index,\n",
" name='Art Campaigns launched since 2009 Per Month')\n",
"\n",
"trace_7 = go.Scatter(x= main_catigs_m_Food.values,\n",
" y= main_catigs_m_Food.index,\n",
" name='Food Campaigns launched since 2009 Per Month')\n",
"\n",
"trace_8 = go.Scatter(x= main_catigs_m_Fashion.values,\n",
" y= main_catigs_m_Fashion.index,\n",
" name='Fashion Campaigns launched since 2009 Per Month')\n",
"\n",
"trace_9 = go.Scatter(x= main_catigs_m_Theater.values,\n",
" y= main_catigs_m_Theater.index,\n",
" name='Theater Campaigns launched since 2009 Per Month')\n",
"\n",
"trace_10 = go.Scatter(x= main_catigs_m_Comics.values,\n",
" y= main_catigs_m_Comics.index,\n",
" name='Comics Campaigns launched since 2009 Per Month')\n",
"\n",
"trace_11 = go.Scatter(x= main_catigs_m_Photography.values,\n",
" y= main_catigs_m_Photography.index,\n",
" name='Photography Campaigns launched since 2009 Per Month')\n",
"\n",
"trace_12 = go.Scatter(x= main_catigs_m_Crafts.values,\n",
" y= main_catigs_m_Crafts.index,\n",
" name='Crafts Campaigns launched since 2009 Per Month')\n",
"\n",
"trace_13 = go.Scatter(x= main_catigs_m_Journalism.values,\n",
" y= main_catigs_m_Journalism.index,\n",
" name='Journalism Campaigns launched since 2009 Per Month')\n",
"\n",
"trace_14 = go.Scatter(x= main_catigs_m_Dance.values,\n",
" y= main_catigs_m_Dance.index,\n",
" name='Dance Campaigns launched since 2009 Per Month')\n",
"\n",
"data = [trace_0, trace_1, trace_2,trace_3,trace_4,trace_5,trace_6,trace_7,trace_8,trace_9,\n",
" trace_10,trace_11,trace_12,trace_13,trace_14]\n",
"\n",
"\n",
"\n",
"updatemenus = list([\n",
" dict(active=-1,\n",
" buttons=list([ \n",
" dict(label = 'All Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [True, True, True, True,\n",
" True, True, True,True,\n",
" True, True, True,True,\n",
" True, True, True]},\n",
" {'title': 'Average Amount raised for All Campaigns launched since 2009 Per Month'}]),\n",
" \n",
" dict(label = 'Film Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [True, False, False, False,\n",
" False, False, False,False,\n",
" False, False, False,False,\n",
" False, False, False]},\n",
" {'title': 'Average Amount raised for Film Campaigns launched since 2009 Per Month'}]),\n",
" \n",
" dict(label = 'Music Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [False, True, False, False,\n",
" False, False, False,False,\n",
" False, False, False,False,\n",
" False, False, False]},\n",
" {'title': 'Average Amount raised for Music Campaigns launched since 2009 Per Month'}]),\n",
" \n",
" dict(label = 'Publishing Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [False, False, True, False,\n",
" False, False, False,False,\n",
" False, False, False,False,\n",
" False, False, False]},\n",
" {'title': 'Average Amount raised for PublishingCampaigns launched since 2009 Per Month'}]),\n",
" \n",
" dict(label = 'Games Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [False, False, False,True, \n",
" False, False, False,False,\n",
" False, False, False,False,\n",
" False, False, False]},\n",
" {'title': 'Average Amount raised for Games Campaigns launched since 2009 Per Month'}]),\n",
" \n",
" dict(label = 'Technology Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [False, False, False, False,\n",
" True, False, False,False,\n",
" False, False, False,False,\n",
" False, False, False]},\n",
" {'title': 'Average Amount raised for Technology Campaigns launched since 2009 Per Month'}]),\n",
" \n",
" dict(label = 'Design Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [False, False, False, False,\n",
" False, True, False,False,\n",
" False, False, False,False,\n",
" False, False, False]},\n",
" {'title': 'Average Amount raised for Design Campaigns launched since 2009 Per Month'}]),\n",
"\n",
" dict(label = 'Art Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [False, False, False, False,\n",
" False, False, True, False,\n",
" False, False, False,False,\n",
" False, False, False]},\n",
" {'title': 'Average Amount raised for Art Campaigns launched since 2009 Per Month'}]),\n",
" \n",
" dict(label = 'Food Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [False, False, False, False,\n",
" False, False, False, True, \n",
" False, False, False,False,\n",
" False, False, False]},\n",
" {'title': 'Average Amount raised for Food Campaigns launched since 2009 Per Month'}]),\n",
" \n",
" dict(label = 'Fashion Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [False, False, False, False, \n",
" False, False, False,False,\n",
" True, False, False,False,\n",
" False, False, False]},\n",
" {'title': 'Average Amount raised for Fashion Campaigns launched since 2009 Per Month'}]),\n",
" \n",
" dict(label = 'Theater Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [False, False, False, False, \n",
" False, False, False,False,\n",
" False, True, False,False,\n",
" False, False, False]},\n",
" {'title': 'Average Amount raised for Theater Campaigns launched since 2009 Per Month'}]),\n",
" \n",
" dict(label = 'Comics Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [False, False, False, False, \n",
" False, False, False,False,\n",
" False, False, True, False,\n",
" False, False, False]},\n",
" {'title': 'Average Amount raised for Comics Campaigns launched since 2009 Per Month'}]),\n",
" \n",
" dict(label = 'Photography Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [False, False, False, False, \n",
" False, False, False,False,\n",
" False, False, False, True, \n",
" False, False, False]},\n",
" {'title': 'Average Amount raised for Photography Campaigns launched since 2009 Per Month'}]),\n",
" \n",
" dict(label = 'Crafts Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [False, False, False, False, \n",
" False, False, False,False,\n",
" False, False, False,False,\n",
" True, False, False]},\n",
" {'title': 'Average Amount raised for Crafts Campaigns launched since 2009 Per Month'}]),\n",
" \n",
" dict(label = 'Journalism Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [False, False, False, False, \n",
" False, False, False,False,\n",
" False, False, False,False,\n",
" False, True, False]},\n",
" {'title': 'Average Amount raised for Journalism Campaigns launched since 2009 Per Month'}]),\n",
" \n",
" dict(label = 'Dance Campaigns launched since 2009 Per Month',\n",
" method = 'update',\n",
" args = [{'visible': [False, False, False, False, \n",
" False, False, False,False,\n",
" False, False, False,False,\n",
" False, False, True]},\n",
" {'title': 'Average Amount raised for Dance Campaigns launched since 2009 Per Month'}])\n",
" ]),\n",
" ),\n",
" dict (direction = 'down',\n",
" pad = {'r': 10, 't': 10},\n",
" showactive = True,\n",
" x = 0.5,\n",
" xanchor = 'center',\n",
" y = 1.12,\n",
" yanchor = 'top')\n",
"])\n",
"\n",
"\n",
"\n",
"layout = go.Layout(\n",
" dict(title='Average Amount raised per Campaign Category in its Most popular Months', \n",
" showlegend=False,\n",
" updatemenus=updatemenus),\n",
" xaxis=dict(\n",
" title='Average Amount raised',\n",
" titlefont=dict(\n",
" family='Ariel',\n",
" size=14,\n",
" color='#7f7f7f'\n",
" )\n",
" ),\n",
" yaxis=dict(\n",
" title='Month of the Year',\n",
" titlefont=dict(\n",
" \n",
" family='Ariel',\n",
" size=14,\n",
" color='#7f7f7f'\n",
" \n",
" )\n",
" )\n",
")\n",
"\n",
"fig = dict(data=data, layout=layout)\n",
"py.iplot(fig, filename='update_dropdown')\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:Anaconda3]",
"language": "python",
"name": "conda-env-Anaconda3-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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