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Pandas: Construct a DataFrame using the MultiIndex levels

Pandas Indexing: Exercise-13 with Solution

Write a Pandas program to construct a DataFrame using the MultiIndex levels as the column and index.

Sample Solution:

Python Code :

import pandas as pd 
import numpy as np
sales_arrays = [['sale1', 'sale1', 'sale2', 'sale2', 'sale3', 'sale3', 'sale4', 'sale4'],
          ['city1', 'city2', 'city1', 'city2', 'city1', 'city2', 'city1', 'city2']]
sales_tuples = list(zip(*sales_arrays))
print("Create a MultiIndex:")
sales_index = pd.MultiIndex.from_tuples(sales_tuples, names=['sale', 'city'])
print(sales_tuples)
print("\nConstruct a Dataframe using the said MultiIndex levels: ")
df = pd.DataFrame(np.random.randn(8, 5), index=sales_index)
print(df)

Sample Output:

Create a MultiIndex:
[('sale1', 'city1'), ('sale1', 'city2'), ('sale2', 'city1'), ('sale2', 'city2'), ('sale3', 'city1'), ('sale3', 'city2'), ('sale4', 'city1'), ('sale4', 'city2')]

Construct a Dataframe using the said MultiIndex levels: 
                    0         1         2         3         4
sale  city                                                   
sale1 city1 -1.020550 -0.809408  0.911425  0.059023  0.495317
      city2 -1.208514  0.039022  0.088428 -0.899280  1.722276
sale2 city1  0.510427 -0.396097 -2.076445  1.080586  1.268495
      city2 -1.158077 -0.892657  1.221519 -0.802645  0.095342
sale3 city1  1.535934  0.432627 -1.307655  0.031096 -0.060850
      city2  0.601156  0.202661 -1.469705 -1.787885  1.285210
sale4 city1  0.455104  0.395752  0.115163  1.188649  0.415450
      city2  0.218054 -0.722397  0.590288 -1.347249 -0.223215

Python Code Editor:


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Previous: Write a Pandas program to construct a series using the MultiIndex levels as the column and index.

Next: Write a Pandas program to extract a single row, rows and a specific value from a MultiIndex levels DataFrame.

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For-else construct useful when searched for something and find it:

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