Run the following code cell. By using df88_1, df88_2 and df88_3, write codes to perform an...

 

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Run the following code cell. By using df88_1, df88_2 and df88_3, write codes to perform an intersecting (inner) join by using column ten as "primary key", name the output dataframe as mydf88, make sure mydf88 has 2 rows and 6 columns. Your codes should make sure the following code cell will print the desired output (shown below) 1: # Run this cell. DO NOT change anything! import pandas as pd import numpy as np from numpy import nan as NA import string rgen = np.random. RandomState (random_state) df88_1 = pd.DataFrame (rgen.randn(20).reshape ((5,4)), index=list('ABCDE'), columns=['ten', 'two', 'three', 'four']) df88_1['ten'] = df88_1.index.tolist () display(df88_1) df88_2= pd.DataFrame (np.arange (50,34,-2). reshape((4,2)), index=np. array(list(string.ascii_uppercase) ) [range(0,12,3)], columns=['ten', 'batman']) df88_2['ten'] = df88_2.index.tolist() display (df88_2) df88_3 = pd.DataFrame(np. array(['MARKET', 'DISC', 'FINA', 'ENERGY', 'ACCT']).reshape ((5,1)), index=df88_1.index.tolist (), columns=['department']) df88_3.reset_index(inplace=True, drop-False) df88_3. rename (columns={'index': 'ten'}, inplace=True) display (df88_3) C D E A B B -2.301539 A D 7 ten 0 G G 3 4 two A -0.611756 -0.528172 -1.072969 1.744812 -0.761207 C -0.249370 1.462108 -2.060141 D -0.384054 1.133769 -1.099891 E -0.877858 0.042214 0.582815 ten batman A ten department A MARKET 1 B 2 с D E 48 44 40 36 three DISC FINA ENERGY ACCT four Run the following code cell. By using df88_1, df88_2 and df88_3, write codes to perform an intersecting (inner) join by using column ten as "primary key", name the output dataframe as mydf88, make sure mydf88 has 2 rows and 6 columns. Your codes should make sure the following code cell will print the desired output (shown below) 1: # Run this cell. DO NOT change anything! import pandas as pd import numpy as np from numpy import nan as NA import string rgen = np.random. RandomState (random_state) df88_1 = pd.DataFrame (rgen.randn(20).reshape ((5,4)), index=list('ABCDE'), columns=['ten', 'two', 'three', 'four']) df88_1['ten'] = df88_1.index.tolist () display(df88_1) df88_2= pd.DataFrame (np.arange (50,34,-2). reshape((4,2)), index=np. array(list(string.ascii_uppercase) ) [range(0,12,3)], columns=['ten', 'batman']) df88_2['ten'] = df88_2.index.tolist() display (df88_2) df88_3 = pd.DataFrame(np. array(['MARKET', 'DISC', 'FINA', 'ENERGY', 'ACCT']).reshape ((5,1)), index=df88_1.index.tolist (), columns=['department']) df88_3.reset_index(inplace=True, drop-False) df88_3. rename (columns={'index': 'ten'}, inplace=True) display (df88_3) C D E A B B -2.301539 A D 7 ten 0 G G 3 4 two A -0.611756 -0.528172 -1.072969 1.744812 -0.761207 C -0.249370 1.462108 -2.060141 D -0.384054 1.133769 -1.099891 E -0.877858 0.042214 0.582815 ten batman A ten department A MARKET 1 B 2 с D E 48 44 40 36 three DISC FINA ENERGY ACCT four

Answer rating: 100% (QA)

To perform an inner join using column ten as the p... View the full answer