Pandas is the most popular Python library that is used for data analysis. It provides highly optimized performance with back-end source code that is purely written in C or Python.
Here we will see how to generate random integers in the Pandas datagram. We will be using the numpy.random.randint() method to generate random integers.
Example 1 : Generating random integers in Pandas Single Data frame column.
# importing pandas and numpy libraries import numpy as np import pandas as pd # generating 11 random integers from 5 to 35 data = np.random.randint(5, 35, size = 11) df = pd.DataFrame(data, columns = ['random_numbers']) # displaying random integers in data frame print(df) |
Output :
Example 2 : Sorting a column in Pandas Single Data frame column.
# importing pandas and numpy libraries import numpy as np import pandas as pd # generating 7 random integers from 5 to 35 data = np.random.randint(5, 35, size = 7) df = pd.DataFrame(data, columns = ['integers']) # displaying random integers in data frame print("Before Sorting :") print(df) # sorting the random integer values # using dataframe.sort_values() # and displaying them df.sort_values("integers", axis = 0, ascending = True, inplace = True, na_position ='last') print("After Sorting :") print(df) |
Output :
Example 3 : Generating random integers in Pandas Multiple Data frame column.
# importing pandas and numpy libraries import numpy as np import pandas as pd # generating 12X3 i.e 36 random integers from 5 to 40 data = np.random.randint(5, 40, size = (12, 3)) df = pd.DataFrame(data, columns = ['random_no_1', 'random_no_2', 'random_no_3']) # displaying random integers in the dataframe print(df) |
Output :
Example 4 : Sorting random integers columns in Pandas Multiple Data frame column.
# importing pandas and numpy libraries import numpy as np import pandas as pd # generating 6x2 i.e 12 random integers # from 5 to 40 data = np.random.randint(5, 40, size = (6, 2)) df = pd.DataFrame(data, columns = ['random_col_1', 'random_col_2']) # displaying random integers in data frame print("Before Sorting :") print(df) # Sorting both Random integer column # First column 1 is sorted # then for every column 1, column 2 is sorted # in ascending order # using dataframe.sort_values() df.sort_values(['random_col_1', 'random_col_2'], axis = 0, ascending = [True, True], inplace = True) print("After Sorting :") print(df) |
Output :
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