在熊猫数据帧中生成随机整数
原文:https://www . geesforgeks . org/generating-random-整数 in-pandas-dataframe/
Pandas 是最流行的用于数据分析的 Python 库。它通过纯 C 或 Python 编写的后端源代码提供了高度优化的性能。
这里我们将看到如何在熊猫数据报中生成随机整数。我们将使用[numpy.random.randint()](https://www.geeksforgeeks.org/numpy-random-rand-python/)
方法生成随机整数。
例 1 : 熊猫单数据帧列生成随机整数。
# 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)
输出:
示例 2 :对熊猫单数据框列中的一列进行排序。
# 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)
输出:
例 3 : 在熊猫多数据帧列中生成随机整数。
# 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)
输出:
示例 4 : 对 Pandas 多数据框列中的随机整数列进行排序。
# 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)
输出:
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