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df.reset_index(inplace=True)
df = df.rename(columns = {'index':'new column name'})
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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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# assignment copy
df = df.set_index('month')
# or inplace
df.set_index('month', inplace=True)
# year sale month month year sale
# 0 2012 55 1 1 2012 55
# 1 2014 40 4 => 4 2014 40
# 2 2013 84 7 7 2013 84
# 3 2014 31 10 10 2014 31
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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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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)
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>>> df.index = [x for x in range(1, len(df.values)+1)]
>>> df
name job score
1 'Pete Houston' 'Software Engineer' 92
2 'John Wick' 'Assassin' 95
3 'Bruce Wayne' 'Batman' 99
4 'Clark Kent' 'Superman' 96