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Annotations of Pandas DataFrame
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FromDict.ipynb
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gen_names_xlsx.ipynb
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gen_random_values.ipynb
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import_xlsx_to_django_database.ipynb
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import_xlsx_to_django_database2.ipynb
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OrcamentosNumOrcNovo.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_excel('Orcamentos.xlsx')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"df = df[['IDOrcamento', 'NumOrc', 'NumOrc2', 'Opcao', 'Ano']]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>IDOrcamento</th>\n",
" <th>NumOrc</th>\n",
" <th>NumOrc2</th>\n",
" <th>Opcao</th>\n",
" <th>Ano</th>\n",
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" <td>2938</td>\n",
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" <th>1</th>\n",
" <td>2940</td>\n",
" <td>1391</td>\n",
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" <td>2016-01-04 14:27:44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2941</td>\n",
" <td>1392</td>\n",
" <td>NaN</td>\n",
" <td>A</td>\n",
" <td>2016-01-04 14:47:23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2942</td>\n",
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" <td>2016-01-04 15:18:16</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" IDOrcamento NumOrc NumOrc2 Opcao Ano\n",
"0 2938 1390 NaN A 2016-01-04 10:01:14\n",
"1 2940 1391 NaN A 2016-01-04 14:27:44\n",
"2 2941 1392 NaN A 2016-01-04 14:47:23\n",
"3 2942 1393 NaN A 2016-01-04 15:03:33\n",
"4 2943 1394 NaN A 2016-01-04 15:18:16"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"datetime.datetime(2016, 1, 4, 10, 1, 14)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Ano'][0].to_pydatetime()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"df['NOrc'] = df['NumOrc'].astype(str) + '.' + df['Opcao'] + '/' + df['Ano'].dt.year.astype(str)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>IDOrcamento</th>\n",
" <th>NumOrc</th>\n",
" <th>NumOrc2</th>\n",
" <th>Opcao</th>\n",
" <th>Ano</th>\n",
" <th>NOrc</th>\n",
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" <td>1450.A/2016</td>\n",
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" <td>1451.A/2016</td>\n",
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" <tr>\n",
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" <td>NaN</td>\n",
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" <td>1305.B/2015</td>\n",
" </tr>\n",
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" <th>78</th>\n",
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" <td>1454</td>\n",
" <td>NaN</td>\n",
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" <td>2016-01-26 15:54:54</td>\n",
" <td>1454.C/2016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>3021</td>\n",
" <td>1454</td>\n",
" <td>NaN</td>\n",
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" <td>2016-01-26 15:54:54</td>\n",
" <td>1454.D/2016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>3022</td>\n",
" <td>1454</td>\n",
" <td>NaN</td>\n",
" <td>E</td>\n",
" <td>2016-01-26 15:54:54</td>\n",
" <td>1454.E/2016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>3023</td>\n",
" <td>1454</td>\n",
" <td>NaN</td>\n",
" <td>F</td>\n",
" <td>2016-01-26 15:54:54</td>\n",
" <td>1454.F/2016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>3024</td>\n",
" <td>1305</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" <td>2015-11-26 15:00:40</td>\n",
" <td>1305.C/2015</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" IDOrcamento NumOrc NumOrc2 Opcao Ano NOrc\n",
"70 3012 1450 NaN A 2016-01-22 08:20:33 1450.A/2016\n",
"71 3013 1451 NaN A 2016-01-22 11:19:26 1451.A/2016\n",
"72 3014 1305 NaN B 2015-11-26 15:00:40 1305.B/2015\n",
"73 3015 1452 NaN A 2016-01-26 10:11:53 1452.A/2016\n",
"74 3016 1394 NaN B 2016-04-01 15:18:16 1394.B/2016\n",
"75 3017 1453 NaN A 2016-01-26 12:26:44 1453.A/2016\n",
"76 3018 1454 NaN A 2016-01-26 15:54:54 1454.A/2016\n",
"77 3019 1454 NaN B 2016-01-26 15:54:54 1454.B/2016\n",
"78 3020 1454 NaN C 2016-01-26 15:54:54 1454.C/2016\n",
"79 3021 1454 NaN D 2016-01-26 15:54:54 1454.D/2016\n",
"80 3022 1454 NaN E 2016-01-26 15:54:54 1454.E/2016\n",
"81 3023 1454 NaN F 2016-01-26 15:54:54 1454.F/2016\n",
"82 3024 1305 NaN C 2015-11-26 15:00:40 1305.C/2015"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.loc[70:82]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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>IDOrcamento</th>\n",
" <th>NumOrc</th>\n",
" <th>NumOrc2</th>\n",
" <th>Opcao</th>\n",
" <th>Ano</th>\n",
" <th>NOrc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3099</th>\n",
" <td>6132</td>\n",
" <td>1429</td>\n",
" <td>NaN</td>\n",
" <td>A</td>\n",
" <td>2018-12-21 11:37:06</td>\n",
" <td>1429.A/2018</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" IDOrcamento NumOrc NumOrc2 Opcao Ano NOrc\n",
"3099 6132 1429 NaN A 2018-12-21 11:37:06 1429.A/2018"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['NOrc'].str.contains('1429.A/2018')]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6132"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['NOrc'].str.contains('1429.A/2018')]['IDOrcamento'].iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"dff = pd.DataFrame(columns=['IDOrcamento', 'NumOrc2', 'NOrc'])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2016\n",
"2016\n",
"2016\n",
"2016\n",
"2016\n"
]
}
],
"source": [
"for row in df.head().itertuples():\n",
" print(str(row.Ano.year))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"for row in df.itertuples():\n",
" try:\n",
" dff = dff.append({'IDOrcamento': row.IDOrcamento, 'NumOrc2': df[df['NOrc'].str.contains(str(row.NumOrc)+'.A/'+str(row.Ano.year))]['IDOrcamento'].iloc[0], 'NOrc': row.NOrc}, ignore_index=True)\n",
" # print(row.IDOrcamento, df[df['NOrc'].str.contains(str(row.NumOrc)+'.A')]['IDOrcamento'].iloc[0]) # , row.NOrc\n",
" except IndexError:\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>IDOrcamento</th>\n",
" <th>NumOrc2</th>\n",
" <th>NOrc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3059</th>\n",
" <td>6128</td>\n",
" <td>6128</td>\n",
" <td>1426.A/2018</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3060</th>\n",
" <td>6129</td>\n",
" <td>6129</td>\n",
" <td>1427.A/2018</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3061</th>\n",
" <td>6130</td>\n",
" <td>6130</td>\n",
" <td>1428.A/2018</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3062</th>\n",
" <td>6131</td>\n",
" <td>6090</td>\n",
" <td>1403.B/2018</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3063</th>\n",
" <td>6132</td>\n",
" <td>6132</td>\n",
" <td>1429.A/2018</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" IDOrcamento NumOrc2 NOrc\n",
"3059 6128 6128 1426.A/2018\n",
"3060 6129 6129 1427.A/2018\n",
"3061 6130 6130 1428.A/2018\n",
"3062 6131 6090 1403.B/2018\n",
"3063 6132 6132 1429.A/2018"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dff.tail()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" 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>IDOrcamento</th>\n",
" <th>NumOrc2</th>\n",
" <th>NOrc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>3016</td>\n",
" <td>2943</td>\n",
" <td>1394.B/2016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>3017</td>\n",
" <td>3017</td>\n",
" <td>1453.A/2016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>3018</td>\n",
" <td>3018</td>\n",
" <td>1454.A/2016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>3019</td>\n",
" <td>3018</td>\n",
" <td>1454.B/2016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>3020</td>\n",
" <td>3018</td>\n",
" <td>1454.C/2016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>3021</td>\n",
" <td>3018</td>\n",
" <td>1454.D/2016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>3022</td>\n",
" <td>3018</td>\n",
" <td>1454.E/2016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>3023</td>\n",
" <td>3018</td>\n",
" <td>1454.F/2016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>3025</td>\n",
" <td>3025</td>\n",
" <td>1455.A/2016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>3026</td>\n",
" <td>2943</td>\n",
" <td>1394.C/2016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>3027</td>\n",
" <td>3027</td>\n",
" <td>1456.A/2016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81</th>\n",
" <td>3028</td>\n",
" <td>3028</td>\n",
" <td>1457.A/2016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>3029</td>\n",
" <td>3029</td>\n",
" <td>1458.A/2016</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" IDOrcamento NumOrc2 NOrc\n",
"70 3016 2943 1394.B/2016\n",
"71 3017 3017 1453.A/2016\n",
"72 3018 3018 1454.A/2016\n",
"73 3019 3018 1454.B/2016\n",
"74 3020 3018 1454.C/2016\n",
"75 3021 3018 1454.D/2016\n",
"76 3022 3018 1454.E/2016\n",
"77 3023 3018 1454.F/2016\n",
"78 3025 3025 1455.A/2016\n",
"79 3026 2943 1394.C/2016\n",
"80 3027 3027 1456.A/2016\n",
"81 3028 3028 1457.A/2016\n",
"82 3029 3029 1458.A/2016"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dff.loc[70:82]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"dff.to_excel('OrcamentosNumOrcNovo.xlsx')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Django Shell-Plus",
"language": "python",
"name": "django_extensions"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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@paulochf
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paulochf commented Jul 16, 2018 •
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Código que substitui da célula [6] em diante do separe_email.ipynb:

emails_df = df.email_.str.split(";").apply(pd.Series).stack().reset_index(level=1, drop=True).to_frame('emails')

dffinal = df.drop("email_", axis=1).merge(emails_df, left_index=True, right_index=True)

Um detalhe é a falta de um índice único dos registros. Então, supondo o índice automático decorrente da importação do arquivo Excel como ID válido, esse ID é o usado no "join" (merge).

@rg3915
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rg3915 commented Aug 1, 2018

Para transformar Query do Django em DataFrame:
df = pd.DataFrame(list(User.objects.all().values()))

@rg3915
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rg3915 commented Aug 1, 2018

Pra mostrar somente algumas colunas:
df[['id', 'email']]

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rg3915 commented Aug 28, 2018

Transformando todos os campos do DataFrame num jSON
df.T.apply(dict).tolist()

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rg3915 commented Aug 28, 2018

Renomeando colunas
df.rename(columns={'old_name': 'new_name'})

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rg3915 commented Aug 28, 2018

Selecionando as colunas de 10 em 10
df.iloc[:, :10]
com transposição
df.T.iloc[:10]

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rg3915 commented Oct 11, 2018

Novo df com algumas colunas.
new = old.filter(['A','B','D'], axis=1)

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rg3915 commented Oct 12, 2018

Como não inserir chaves com valor nulo no dicionário:
df.T.apply(lambda x: dict(x.dropna())).tolist()

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rg3915 commented Oct 17, 2018

Retornando os valores cujo tamanho da string seja maior que...
df[df['foo'].str.len() > 50]['foo']

Mostrando o tamanho de cada célula
df[df['foo'].str.len() > 50]['foo'].str.len()

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rg3915 commented Nov 9, 2018 •
edited
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Mostrando o tamanho de cada célula da coluna

df['foo_len'] = df['foo'].apply(len)
df[['foo_len', 'foo']]

Ou

df['foo'].str.len()

@rg3915
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rg3915 commented Dec 5, 2018

Suponha que você tenha Cliente e Obra.

Pegando o ID do Cliente que está em Obra e trocando pelo nome do Cliente que está no outro DataFrame.

# JSON do Cliente com IDCliente e Cliente
dict_cliente = df_cliente[['IDCliente', 'Cliente']].T.apply(dict).tolist()

[
 {'IDCliente': 288, 'Cliente': 'Cliente Um'},
 {'IDCliente': 1, 'Cliente': 'Cliente Dois'},
 {'IDCliente': 959, 'Cliente': 'Cliente Três'},
]

Montando o dicionário que será usado como busca de cada Cliente a partir do seu ID.

_dict_cliente = {}
for item in dict_cliente:
    _dict_cliente[item['IDCliente']] = item['Cliente']

_dict_cliente

{
 288: 'Cliente Um',
 1: 'Cliente Dois',
 959: 'Cliente Três',
}

A partir desse dicionário fazemos a busca no outro DataFrame.

for row in df.itertuples():
    nome_cliente = _dict_cliente.get(row.IDCliente)
    print(row.IDCliente, nome_cliente)

288 Cliente Um
1   Cliente Dois
959 Cliente Três

@rg3915
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rg3915 commented Dec 9, 2018 •
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Retornando o valor máximo agrupado por ano.

df.groupby(['Ano'])['NumeroOrcamento'].max()
df.reset_index()

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rg3915 commented Dec 10, 2018

Verificando data vazia:

for row in df.itertuples():
    if row.DataOrcamento is pd.NaT:
        print('Vazio')
    else:
        print(row.DataOrcamento)

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rg3915 commented Dec 16, 2018

Definindo vários fillna diferentes por coluna:

values = {'last_name': '', 'occupation': '', 'age': 0}
df = df.fillna(value=values)
df.head()

@rg3915
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rg3915 commented Aug 6, 2019

Se tiver problema com liblzma, faça um downgrade do Pandas para pandas==0.24.2.
https://stackoverflow.com/a/57115325

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rg3915 commented Aug 6, 2019

Retorna o tamanho do maior objeto de cada coluna.

dict_sizes = {}
for col in df.columns:
    try:
        print(f'{col} max length: {df[col].map(len).max()}\n')
        dict_sizes[col] = df[col].map(len).max()
    except Exception as e:
        raise e
dict_sizes

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rg3915 commented Sep 12, 2019

from_dict doc

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rg3915 commented Nov 6, 2019

dtype example
df['estoque'] =df['estoque'].fillna(0).astype(int)

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rg3915 commented Nov 6, 2019

Pandas Dataframe df to Django
https://www.laurivan.com/save-pandas-dataframe-as-django-model/

Produto.objects.bulk_create(
    Produto(**item) for item in df.to_dict('records')
)

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rg3915 commented Nov 13, 2019

Definindo os tipos das colunas com dtype

dict_types_annot = {
    'produto': str,
    'ncm': str,
    'preco': float,
    'estoque': 'Int64',
}

# Define os tipos das colunas
dff = df.astype(dict_types_annot, errors='ignore')

# Troca 'nan' por None e float por None.
dff = dff.replace({'nan': None, float('nan'): None})

dff.to_dict('records')

Produto.objects.bulk_create(
    Produto(**item) for item in dff.to_dict('records')
)

@rg3915
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rg3915 commented Jan 22, 2020

Intersecção de dataframes

import pandas as pd
import numpy as np
import datetime
from random import randint

df1 = pd.DataFrame({
    'letters': ['A', 'B', 'C', 'D', 'E', 'J', 'K', 'M'],
    'B': np.random.randint(0, 10, 8),
})
df1

df2 = pd.DataFrame({
    'letters': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'U', 'Z'],
    'B': np.random.randint(0, 10, 26),
})
df2

# Retorna o que tem de comum nos dois dataframes.
pd.merge(df1, df2, how='inner', on='fruits')

# Retorna o que tem de comum, considerando o df1.
pd.merge(df1, df2, how='left', on='fruits')

# Retorna o que tem de comum, considerando o df2.
pd.merge(df1, df2, how='right', on='fruits')

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