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Question 8

To convert a DataFrame from long to wide, you can use the pivot function. The first step is to assign a number to each row using the cumcount method of the groupby object. Then, use this new column as the index and pivot on the two columns you want to transform.

import pandas as pd

# create a sample dataframe
df = pd.DataFrame({
    'A': ['a', 'a', 'a', 'b', 'b'],
    'B': [0, 11, 2, 10, 14],
    'C': [7, None, None, None, None]
})

# assign a number to each row
df['count'] = df.groupby('A').cumcount()

# pivot on the two columns you want to transform
df_pivot = df.pivot(index='count', columns='A', values='B')

Alternatively, you can use the pivot_table function with an index specified as a Series.

df_pivot = df.pivot_table(index=df['count'], columns='A', values='B')

Question 9

To flatten the multiple index to single index after pivoting, you need to specify the column names in the format of index=column1|column2 if the column names are strings that need to be joined with a pipe character (|). Alternatively, you can use the {0[0]}|{0[1]} format to join the column names.

df.columns = df.columns.map('|'.join)

Or,

df.columns = df.columns.map('{0[0]}|{0[1]}.format')

Question 10

To convert a DataFrame from long to wide, you can use the pivot function with the values parameter specified as the name of the column in the original DataFrame that contains the values.

df_pivot = df.pivot(index='A', columns='B', values=df['C'])

Alternatively, you can use the pivot_table function with an index specified as a Series and the columns parameter specified as a list of column names.

df_pivot = df.pivot_table(index=df['A'], columns=df['B'], values=df['C'])

Question 11

To convert a DataFrame from long to wide, you can use the pivot_longer function from the pandas library.

import pandas as pd

# create a sample dataframe
df = pd.DataFrame({
    'name': ['John', 'Mary', 'Jane'],
    'age': [25, 31, 22],
    'city': ['New York', 'Los Angeles', 'Chicago']
})

# pivot on the two columns you want to transform
df_wide = df.pivot_longer('city', col_names=['city_name'], value_name='value')

Alternatively, you can use the pivot function with the values parameter specified as a list of column names.

df_pivot = df.pivot(index='name', columns='city', values=['age'])

Note that in this case, we need to specify all the columns in the original DataFrame that contain the values, not just two.

Last modified on 2023-06-24