RENAME SPECIFIC COLUMNS Use the df.rename() function and refer the columns to be renamed. Not all the columns have to be renamed: df = df.rename(columns={‘oldName1’: ‘newName1’, ‘oldName2’: ‘newName2’}) # Or rename the existing DataFrame (rather than creating a copy) df.rename(columns={‘oldName1’: ‘newName1’, ‘oldName2’: ‘newName2’}, inplace=True) Minimal Code Example df = pd.DataFrame(‘x’, index=range(3), columns=list(‘abcde’)) df a b … Read more
To select rows whose column value equals a scalar, some_value, use ==: df.loc[df[‘column_name’] == some_value] To select rows whose column value is in an iterable, some_values, use isin: df.loc[df[‘column_name’].isin(some_values)] Combine multiple conditions with &: df.loc[(df[‘column_name’] >= A) & (df[‘column_name’] <= B)] Note the parentheses. Due to Python’s operator precedence rules, & binds more tightly than … Read more
DataFrame.iterrows is a generator which yields both the index and row (as a Series): import pandas as pd df = pd.DataFrame({‘c1’: [10, 11, 12], ‘c2’: [100, 110, 120]}) df = df.reset_index() # make sure indexes pair with number of rows for index, row in df.iterrows(): print(row[‘c1’], row[‘c2’]) 10 100 11 110 12 120