在熊猫-Python 中找到一列的指数

原文:https://www . geeksforgeeks . org/find-熊猫柱指数-python/

让我们看看如何在熊猫数据框中找到一列的指数。首先，让我们创建一个数据框架:

Python 3

# importing pandas and
# numpy libraries
import pandas as pd
import numpy as np

# creating and initializing a list
values= [ ['Rohan', 5, 50.59], ['Elvish', 2, 90.57],
         ['Deepak', 10, 98.51], ['Soni', 4, 40.24],
         ['Radhika', 1, 99.05], ['Vansh', 15, 85.56] ]

# creating a pandas dataframe
df = pd.DataFrame(values, columns = ['Name',
                                     'University_Rank',
                                     'University_Marks'])

# displaying the data frame
df

输出:

利用numpy . exp()函数求出任意一列的指数。该函数计算输入数组/序列的指数。

*语法:* numpy.exp(数组，out = None，其中= True，casting = 'same_kind '，order = 'K '，dtype = None)

*返回:*输入数组/序列中所有元素指数的数组。

*例 1:* 求单列的指数(整数值)。

Python 3

# importing pandas and
# numpy libraries
import pandas as pd
import numpy as np

# creating and initializing a list
values= [ ['Rohan', 5, 50.59], ['Elvish', 2, 90.57],
         ['Deepak', 10, 98.51], ['Soni', 4, 40.24],
         ['Radhika', 1, 99.05], ['Vansh', 15, 85.56] ]

# creating a pandas dataframe
df = pd.DataFrame(values, columns = ['Name',
                                     'University_Rank',
                                     'University_Marks'])

# finding the exponential value
# of column using np.exp() function
df['exp_value'] = np.exp(df['University_Rank'])

# displaying the data frame
df

*输出:*

*例 2:* 求单列的指数(浮点值)。

Python 3

# importing pandas and
# numpy libraries
import pandas as pd
import numpy as np

# creating and initializing a list
values= [ ['Rohan', 5, 50.59], ['Elvish', 2, 90.57],
         ['Deepak', 10, 98.51], ['Soni', 4, 40.24],
         ['Radhika', 1, 99.05], ['Vansh', 15, 85.56] ]

# creating a pandas dataframe
df = pd.DataFrame(values, columns = ['Name',
                                     'University_Rank',
                                     'University_Marks'])

# finding the exponential value
# of column  using np.exp() function
df['exp_value'] = np.exp(df['University_Marks'])

# displaying the data frame
df

*输出:*

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