Add new column based on condition on some other column in pandas.
# np.where(condition, value if condition is true, value if condition is false)
df['hasimage'] = np.where(df['photos']!= '[]', True, False)
df.head()Add new column based on condition on some other column in pandas.
# np.where(condition, value if condition is true, value if condition is false)
df['hasimage'] = np.where(df['photos']!= '[]', True, False)
df.head()python add columns to dataframe without changing the original
#given an original pandas.DataFrame named 'df', and the data series that we want to associate with the new column named 's', 3 possible solutions:
#1-Use "assign()" to create new column and then assign values to it
df_w_new_col = df.assign (name='New_Col')
#2-create an aditional Dataframe with just the new column to add and concatenate with the old dataframe. Like a great majority of pandas methods, this actually creates a new df, it does not just concatenate to the old one so it is safe to change the old and new df's independetly
df = pd.concat( [df, pd.DataFrame({'New_Col:s})], axis=1 )
#3-explicitely create a copy and append a column
df2 = df.copy()
df2['New_Col'] = sCopyright © 2021 Codeinu
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