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# method A
df = df.set_index('col')
# method B
df['col'] = df.index
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df = pd.DataFrame({'month': [1, 4, 7, 10],
'year': [2012, 2014, 2013, 2014],
'sale': [55, 40, 84, 31]})
df.set_index('month')
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import pandas as pd
# Set column as index
df = df.set_index("column")
# Display DataFrame
print(df)
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df.reset_index(inplace=True)
df = df.rename(columns = {'index':'new column name'})
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df.set_index('col') # Set a single column as index
df.set_index(['col1','col2']) # Create multi-level index
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import pandas as pd
# Create a sample DataFrame
data = {'Name': ['John', 'Emma', 'Alex'],
'Age': [25, 28, 32],
'Country': ['USA', 'Canada', 'UK']}
df = pd.DataFrame(data)
# Set 'Name' column as the index column
df.set_index('Name', inplace=True)
# Display the updated DataFrame
print(df)
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#cree un indice par defaut sur la base de donnee
df.reset_index()
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import pandas as pd
# Assuming the dataframe is already created and named 'df'
# Set a single column as the index
df.set_index('column_name', inplace=True)
# Set multiple columns as the index (creating a MultiIndex)
df.set_index(['column_name1', 'column_name2'], inplace=True)
# Reset the index to the default integer index
df.reset_index(inplace=True)