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)
|
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)
|
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)
|
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)
|
Output :
