统计熊猫数据框中的所有行或满足某些条件的行
原文:https://www . geesforgeks . org/count-all-row-or-那些满足熊猫某些条件的人-dataframe/
让我们看看如何计算数据框中的所有行数或熊猫中满足条件的行数。
Dataframe.shape 返回数据帧/系列的形状元组(行、列)。
让我们创建一个熊猫数据框。
# import pandas library as pd
import pandas as pd
# List of Tuples
students = [('Ankit', 22, 'Up', 'Geu'),
('Ankita', 31, 'Delhi', 'Gehu'),
('Rahul', 16, 'Tokyo', 'Abes'),
('Simran', 41, 'Delhi', 'Gehu'),
('Shaurya', 33, 'Delhi', 'Geu'),
('Harshita', 35, 'Mumbai', 'Bhu' ),
('Swapnil', 35, 'Mp', 'Geu'),
('Priya', 35, 'Uk', 'Geu'),
('Jeet', 35, 'Guj', 'Gehu'),
('Ananya', 35, 'Up', 'Bhu')
]
# Create a DataFrame object from
# list of tuples with columns
# and indices.
details = pd.DataFrame(students, columns =['Name', 'Age',
'Place', 'College'],
index =['a', 'b', 'c', 'd', 'e',
'f', 'g', 'i', 'j', 'k'])
details
输出:
代码:统计所有行
# import pandas library as pd
import pandas as pd
# List of Tuples
students = [('Ankit', 22, 'Up', 'Geu'),
('Ankita', 31, 'Delhi', 'Gehu'),
('Rahul', 16, 'Tokyo', 'Abes'),
('Simran', 41, 'Delhi', 'Gehu'),
('Shaurya', 33, 'Delhi', 'Geu'),
('Harshita', 35, 'Mumbai', 'Bhu' ),
('Swapnil', 35, 'Mp', 'Geu'),
('Priya', 35, 'Uk', 'Geu'),
('Jeet', 35, 'Guj', 'Gehu'),
('Ananya', 35, 'Up', 'Bhu')
]
# Create a DataFrame object from
# list of tuples with columns
# and indices.
details = pd.DataFrame(students, columns =['Name', 'Age',
'Place', 'College'],
index =['a', 'b', 'c', 'd', 'e',
'f', 'g', 'i', 'j', 'k'])
# 0th index of tuple returned by shape
# attribute give the number
# of rows in a given dataframe
num_rows = details.shape[0]
print('Number of Rows in given dataframe : ',
num_rows)
输出:
Number of Rows in given dataframe : 10
2)使用数据帧索引T3 统计熊猫数据帧中的所有行
Dataframe.index 属性给出了一系列索引或行标签。
代码:
import pandas as pd
# List of Tuples
students = [('Ankit', 22, 'Up', 'Geu'),
('Ankita', 31, 'Delhi', 'Gehu'),
('Rahul', 16, 'Tokyo', 'Abes'),
('Simran', 41, 'Delhi', 'Gehu'),
('Shaurya', 33, 'Delhi', 'Geu'),
('Harshita', 35, 'Mumbai', 'Bhu' ),
('Swapnil', 35, 'Mp', 'Geu'),
('Priya', 35, 'Uk', 'Geu'),
('Jeet', 35, 'Guj', 'Gehu'),
('Ananya', 35, 'Up', 'Bhu')
]
# Create a DataFrame object from
# list of tuples with columns
# and indices.
details = pd.DataFrame(students, columns =['Name', 'Age',
'Place', 'College'],
index =['a', 'b', 'c', 'd', 'e',
'f', 'g', 'i', 'j', 'k'])
# count number of rows in given dataframe
# by finding the length of indices
num_rows = len(details.index)
print('Number of Rows in given dataframe : ',
num_rows)
输出:
Number of Rows in given dataframe : 10
3)使用数据框应用()对熊猫数据框中满足条件的行进行计数。T3】
Dataframe.apply() ，对数据帧的所有行应用函数，找出行的元素是否满足条件，根据结果返回一个布尔序列。
代码:
# import pandas library as pd
import pandas as pd
# List of Tuples
students = [('Ankit', 22, 'Up', 'Geu'),
('Ankita', 31, 'Delhi', 'Gehu'),
('Rahul', 16, 'Tokyo', 'Abes'),
('Simran', 41, 'Delhi', 'Gehu'),
('Shaurya', 33, 'Delhi', 'Geu'),
('Harshita', 35, 'Mumbai', 'Bhu' ),
('Swapnil', 35, 'Mp', 'Geu'),
('Priya', 35, 'Uk', 'Geu'),
('Jeet', 35, 'Guj', 'Gehu'),
('Ananya', 35, 'Up', 'Bhu')
]
# Create a DataFrame object from
# list of tuples with columns
# and indices.
details = pd.DataFrame(students, columns =['Name', 'Age',
'Place', 'College'],
index =['a', 'b', 'c', 'd', 'e',
'f', 'g', 'i', 'j', 'k'])
# Get a bool series representing which row
# satisfies the condition i.e. True for
# row in which 'College' is 'Geu'
details = details.apply(lambda x : True
if x['College'] == "Geu" else False, axis = 1)
# Count number of True in the series
num_rows = len(details[details == True].index)
print('Number of Rows in dataframe in which College is Geu : ',
num_rows )
输出:
Number of Rows in dataframe in which College is Geu : 4
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