计算 Pandas Dataframe 中的值

在本文中，我们将计算 Pandas 数据框中的值。首先，我们将创建一个数据框，然后我们将计算不同属性的值。

Syntax: DataFrame.count(axis=0, level=None, numeric_only=False)

Parameters:

• axis {0 or ‘index’, 1 or ‘columns’}: default 0 Counts are generated for each column if axis=0 or axis=’index’ and counts are generated for each row if axis=1 or axis=”columns”.
• level (nt or str, optional): If the axis is a MultiIndex, count along a particular level, collapsing into a DataFrame. A str specifies the level name.
• numeric_only (boolean, default False): It includes only int, float or boolean value.

Returns: It returns count of non-null values and if level is used it returns dataframe

循序渐进的方法：

第 1 步：导入库。

Python3

# importing libraries
import numpy as np
import pandas as pd

第 2 步：创建数据框

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# Creating dataframe with
# some missing values
NaN = np.nan
dataframe = pd.DataFrame({'Name': ['Shobhit', 'Vaibhav',
                                   'Vimal', 'Sourabh',
                                   'Rahul', 'Shobhit'],
                          'Physics': [11, 12, 13, 14, NaN, 11],
                          'Chemistry': [10, 14, NaN, 18, 20, 10],
                          'Math': [13, 10, 15, NaN, NaN, 13]})
 
display(dataframe)

输出：

创建数据框

第 3 步：在这一步中，我们只是简单地使用.count()函数来计算不同列的所有值。

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# using dataframe.count()
# to count all values
dataframe.count()

输出：

我们可以看到计数值存在差异，因为我们有缺失值。 Name 列中有 5 个值，物理和化学中有 4 个值，数学中有 3 个值。在这种情况下，它使用带有默认值的参数。

第 4 步：如果我们想计算关于行的所有值，那么我们必须传递轴 = 1 或“列”。

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# we can pass either axis=1 or
# axos='columns' to count with respect to row
print(dataframe.count(axis = 1))
 
print(dataframe.count(axis = 'columns'))

输出：

相对于行计数

第 5 步：现在，如果我们想对数据框中的空值进行计数。

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# it will give the count
# of individual columns count of null values
print(dataframe.isnull().sum())
 
# it will give the total null
# values present in our dataframe
print("Total Null values count: ",
      dataframe.isnull().sum().sum())

输出：

第 6 步： 。一些使用.count()的例子

现在我们要计算物理分数大于 11 的学生人数。

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# count of student with greater
# than 11 marks in physics
print("Count of students with physics marks greater than 11 is->",
      dataframe[dataframe['Physics'] > 11]['Name'].count())
 
# resultant of above dataframe
dataframe[dataframe['Physics']>11]

输出：

物理>11

物理成绩大于10、化学成绩大于11、数学成绩大于9的学生人数。

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# Count of students whose physics marks
# are greater than 10,chemistry marks are
# greater than 11 and math marks are greater than 9.
print("Count of students ->",
      dataframe[(dataframe['Physics'] > 10) &
                (dataframe['Chemistry'] > 11) &
                (dataframe['Math'] > 9)]['Name'].count())
 
# dataframe of above result
dataframe[(dataframe['Physics'] > 10 ) &
          (dataframe['Chemistry'] > 11 ) &
          (dataframe['Math'] > 9 )]

输出：

物理>10，化学>11，数学>9

下面是完整的实现：

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# importing Libraries
import pandas as pd
import numpy as np
 
# Creating dataframe using dictionary
NaN = np.nan
dataframe = pd.DataFrame({'Name': ['Shobhit', 'Vaibhav',
                                   'Vimal', 'Sourabh',
                                   'Rahul', 'Shobhit'],
                          'Physics': [11, 12, 13, 14, NaN, 11],
                          'Chemistry': [10, 14, NaN, 18, 20, 10],
                          'Math': [13, 10, 15, NaN, NaN, 13]})
 
print("Created Dataframe")
print(dataframe)
 
# finding Count of all columns
print("Count of all values wrt columns")
print(dataframe.count())
 
# Count according to rows
print("Count of all values wrt rows")
print(dataframe.count(axis=1))
print(dataframe.count(axis='columns'))
 
# count of null values
print("Null Values counts ")
print(dataframe.isnull().sum())
print("Total null values",
      dataframe.isnull().sum().sum())
 
# count of student with greater
# than 11 marks in physics
print("Count of students with physics marks greater than 11 is->",
      dataframe[dataframe['Physics'] > 11]['Name'].count())
 
# resultant of above dataframe
print(dataframe[dataframe['Physics'] > 11])
print("Count of students ->",
      dataframe[(dataframe['Physics'] > 10) &
                (dataframe['Chemistry'] > 11) &
                (dataframe['Math'] > 9)]['Name'].count())
 
print(dataframe[(dataframe['Physics'] > 10) &
                (dataframe['Chemistry'] > 11) &
                (dataframe['Math'] > 9)])

输出：