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# Replace missing values
df["col"].replace(np.nan, new_val)
# Replace other values
df["col"].str.replace('old','new')
# Replace using masking
df.loc[df['col']=='old', 'col'] = 'new'
# Replace all specified value in the dataframe
df_replaced = df.replace(1, 0)
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df.loc[df['column_name'] == value_you_want_replaced, 'column_name'] = your_value
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# change value in column_where_to_change and in the row where column_name == column_value
df.loc[df['<column_name>']=='column_value', '<column_where_to_change>'] = '<new_value>'
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energy['Country'] = energy['Country'].replace(['Afghanistan','Albania'],['Sa','lol'])
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# replace() syntax
DataFrame.replace(to_replace="<the_value_you_want_to_replace>", value="<new_value_for_input>", inplace=False, limit=None, regex=False, method='pad')