Sure! Here is the code for the requested functions:
import pandas as pd
def concatenate_dataframes(df1, df2):
df_con = pd.merge(df1, df2, on='B', suffixes=('_first', '_second'))
return df_con
df5 = make_df('ABC', [1, 2])
df6 = make_df('BCD', [1, 2])
print('Dataframes before concatenation:')
display(df5, df6)
df_con = concatenate_dataframes(df5, df6)
print('Dataframe after concatenation:')
display(df_con)
def select_idx(df):
df_sel = df.loc[1]
return df_sel
df_sel = select_idx(df_con)
print('Values after selection:')
display(df_sel)
def replace_missing_values(population):
population['population'] = population.groupby(['year', 'ages'])['population'].transform(lambda x: x.fillna(x.mean()))
return population
population = pd.DataFrame({
'ages': ['under18','total','under18','total','under18', 'total',
'under18','total','under18','total','under18','total'],
'year': [2010,2010,2012,2012,2010,2010,
2012,2012,2010,2010, 2012, 2012],
'population': [11174,48175,np.nan,98723,np.nan,
31400,23654,np.nan,
16370,92653,np.nan ,89921]
})
replaced_population = replace_missing_values(population)
print('Population dataframe after replacing missing values:')
display(replaced_population)
Please note that I assumed you have already defined a function make_df to create the sample DataFrames.
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