datagy

import pandas as pd

df = pd.DataFrame({
    'Name': ['Nik', 'Kate', 'Evan'],
    'Location': ['Toronto, ON', 'Atlanta, GA', 'Portland, OR'],
    'Total': [99.99, 125.65, 33.43]
})

df['Over 100'] = ['Yes' if Total > 100 else 'No' for Total in df['Total']]
df[['City', 'State']] = df['Location'].str.split(', ', expand=True)

datagy

# Using text-to-columns in Pandas
import pandas as pd

df = pd.DataFrame({
    'Name': ['Nik', 'Kate', 'Evan'],
    'Location': ['Toronto, ON', 'Atlanta, GA', 'Portland, OR'],
    'Total': [99.99, 125.65, 33.43]
})

df['Over 100'] = ['Yes' if Total > 100 else 'No' for Total in df['Total']]

datagy

# Using if Statements in Python
import pandas as pd

df = pd.DataFrame({
    'Name': ['Nik', 'Kate', 'Evan'],
    'Location': ['Toronto, ON', 'Atlanta, GA', 'Portland, OR'],
    'Total': [99.99, 125.65, 33.43]
})

df['Over 100'] = ['Yes' if Total > 100 else 'No' for Total in df['Total']]

datagy

# Loading a Sample Pandas DataFrame
import pandas as pd

df = pd.DataFrame({
    'Name': ['Nik', 'Kate', 'Evan'],
    'Location': ['Toronto, ON', 'Atlanta, GA', 'Portland, OR'],
    'Total': [99.99, 125.65, 33.43]
})

print(df)

datagy

{
  "If Name == Main": {
	"prefix": "ifn",
	"body": [
	  "if __name__ == '__main__':",
	  "  $0"
	],
	"description": "If Name == Main"
  }
}

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{
"Filter a Pandas DataFrame": {
"prefix": "pf",
"body": [
  "${1:df} = ${1:df}[${1:df}['${2:Column}'] ${3|==, >, >=, <, <=, !=|} ${4:Filter}] "
],
"description": "Filter a Pandas DataFrame"
	  }
}

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{
"Modify a Pandas Column": {
	"prefix": "pdc",
	"body": [
	  "${1:df_name}['${2:col_name}'] = ${1:df_name}['${2:col_name}'].$0"
	],
	"description": "Modifies a Pandas Column"
  }
}

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{
	// "Print to console": {
	// 	"prefix": "log",
	// 	"body": [
	// 		"console.log('$1');",
	// 		"$2"
	// 	],
	// 	"description": "Log output to console"
	// }
}

datagy

# Reading Multiple Columns As Date Time
import pandas as pd

df = pd.read_excel(
    'Pandas_read_files_tips.xlsx', 
    parse_dates={'Date': ['Month', 'Day', 'Year']}
    )
print(df)

# Returns:

datagy

import pandas as pd
import os

# Create list of files
file_path = '/Desktop/Files/'
files = [os.path.join(file_path, file) for file in os.listdir(file_path)]

# Join DataFrames Using pd.concat()
df = pd.concat([pd.read_excel(file) for file in files], ignore_index=True)
print(df.head())