Data Structures in Python Pandas Assignment
Table of Content:
1. Python Pandas | 1 | Data Structures in Pandas
Task 1
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Create a series named heights_A with values 176.2, 158.4, 167.6, 156.2, and 161.4. These values represent the height of 5 students of class A.
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Label each student as s1, s2, s3, s4, and s5.
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Determine the shape of heights_A and display it.
Note: Use the Series method available in the pandas library.
#import the pandas library and aliasing as pd import pandas as pd # Creating the Series heights_A = pd.Series([ 176.2, 158.4, 167.6, 156.2, 161.4 ]) # Creating the row axis labels heights_A.index = ['s1', 's2', 's3', 's4','s5'] # return the shape heights_A.shape # Print the series print (heights_A)
Task 2
- Create another series named weights_A with values 85.1, 90.2, 76.8, 80.4, and 78.9. These values represent the weights of 5 students of class A.
- Label each student as s1, s2, s3, s4, and s5.
- Determine data type of values in weights_A and display it.
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Note: Use the Series method available in the pandas library.
#import the pandas library and aliasing as pd import pandas as pd # Creating the Series weights_A = pd.Series([85.1, 90.2, 76.8, 80.4 , 78.9]) # Creating the row axis labels weights_A.index = ['s1', 's2', 's3', 's4','s5'] # Determine data type weights_A.dtypes # Print the series print (weights_A)
Task 3
- Create a dataframe named df_A, which contains the height and weight of five students namely s1, s2, s3, s4 and s5.
- Label the columns as Student_height and Student_weight, respectively.
- Display the shape of df_A.
Note: Use the DataFrame method in pandas, and also the series heights_A, weights_A created in the previous problems.
#import the pandas library and aliasing as pd import pandas as pd # Creating the Series heights_A = pd.Series([ 176.2, 158.4, 167.6, 156.2, 161.4 ]) # Creating the row axis labels heights_A.index = ['s1', 's2', 's3', 's4','s5'] # Creating the Series weights_A = pd.Series([85.1, 90.2, 76.8, 80.4 , 78.9]) # Creating the row axis labels weights_A.index = ['s1', 's2', 's3', 's4','s5'] df_A = pd.DataFrame() df_A['Student_height'] = heights_A df_A['Student_weight'] = weights_A # Display the shape of dataframe df_A df_A.shape # Print the dataframe print (df_A)
Task 4
- Create another series named heights_B from a 1-D numpy array of 5 elements derived from the normal distribution of mean 170.0 and standard deviation 25.0.
Note: Set random seed to 100 before creating the heights_B series.
- Create another series named weights_B from a 1-D numpy array of 5 elements derived from the normal distribution of mean 75.0 and standard deviation 12.0.
Note: Set random seed to 100 before creating the weights_B series.
- Label both series elements as s1, s2, s3, s4 and s5.
- Print the mean of series heights_B.
Task 5
- Create a dataframe df_B containing the height and weight of students s1, s2, s3, s4 and s5 belonging to class B.
- Label the columns as Student_height and Student_weight respectively.
- Display the column names of df_B.
Note: Use the heights_B and weights_B series created in the above tasks.
Task 6
- Create a panel p, containing the previously created two dataframes df_A and df_B.
- Label the first dataframe as ClassA, and second as ClassB.
- Determine the shape of panel p and display it.
Note: Use the Panel method of pandas.