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.
Generating random integers under a Single Data frame column
Generating 11 random integers from 5 to 35.
python3
import numpy as np
import pandas as pd
data = np.random.randint( 5 , 35 , size = 11 )
df = pd.DataFrame(data, columns = [ 'random_numbers' ])
print (df)
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Output :
Sorting a column under the Single Data frame column
Sorting the random integer values using dataframe.sort_values() and displaying them.
python3
import numpy as np
import pandas as pd
data = np.random.randint( 5 , 35 , size = 7 )
df = pd.DataFrame(data, columns = [ 'integers' ])
print ( "Before Sorting :" )
print (df)
df.sort_values( "integers" , axis = 0 , ascending = True ,
inplace = True , na_position = 'last' )
print ( "After Sorting :" )
print (df)
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Output :

Generating random integers under the Multiple Data frame column
Generating 12X3 i.e 36 random integers from 5 to 40.
python3
import numpy as np
import pandas as pd
data = np.random.randint( 5 , 40 , size = ( 12 , 3 ))
df = pd.DataFrame(data, columns = [ 'random_no_1' ,
'random_no_2' ,
'random_no_3' ])
print (df)
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Output :

Sorting random integers columns in Pandas Multiple Data frame column
Sorting both Random integer columns, First column 1 is sorted then for every column 1, column 2 is sorted in ascending order using dataframe.sort_values().
python3
import numpy as np
import pandas as pd
data = np.random.randint( 5 , 40 , size = ( 6 , 2 ))
df = pd.DataFrame(data, columns = [ 'random_col_1' , 'random_col_2' ])
print ( "Before Sorting :" )
print (df)
df.sort_values([ 'random_col_1' , 'random_col_2' ], axis = 0 ,
ascending = [ True , True ], inplace = True )
print ( "After Sorting :" )
print (df)
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Output :
