在 Python 中循环或迭代数据框的所有或某些列-熊猫
在本文中，我们将讨论如何循环或迭代数据框的全部或某些列？有各种各样的方法来完成这项任务。 我们先创建一个数据框，看看: 代码:
Python 3
# import pandas package
import pandas as pd
# List of Tuples
students = [('Ankit', 22, 'A'),
('Swapnil', 22, 'B'),
('Priya', 22, 'B'),
('Shivangi', 22, 'B'),
]
# Create a DataFrame object
stu_df = pd.DataFrame(students, columns =['Name', 'Age', 'Section'],
index =['1', '2', '3', '4'])
stu_df
输出:
现在让我们来看看迭代数据框的某些列的不同方式: 方法#1: 使用 数据框. iteritems(): 数据框类提供了一个成员函数 ITER items()，它给出了一个迭代器，可以用来迭代数据框的所有列。对于数据帧中的每一列，它都返回一个包含列名及其内容的元组的迭代器。 代码:
Python 3
import pandas as pd
# List of Tuples
students = [('Ankit', 22, 'A'),
('Swapnil', 22, 'B'),
('Priya', 22, 'B'),
('Shivangi', 22, 'B'),
]
# Create a DataFrame object
stu_df = pd.DataFrame(students, columns =['Name', 'Age', 'Section'],
index =['1', '2', '3', '4'])
# gives a tuple of column name and series
# for each column in the dataframe
for (columnName, columnData) in stu_df.iteritems():
print('Column Name : ', columnName)
print('Column Contents : ', columnData.values)
输出:
方法#2: 使用[ ]运算符: 我们可以迭代列名并选择所需的列。 代码:
Python 3
import pandas as pd
# List of Tuples
students = [('Ankit', 22, 'A'),
('Swapnil', 22, 'B'),
('Priya', 22, 'B'),
('Shivangi', 22, 'B'),
]
# Create a DataFrame object
stu_df = pd.DataFrame(students, columns =['Name', 'Age', 'Section'],
index =['1', '2', '3', '4'])
# Iterate over column names
for column in stu_df:
# Select column contents by column
# name using [] operator
columnSeriesObj = stu_df[column]
print('Column Name : ', column)
print('Column Contents : ', columnSeriesObj.values)
输出:
方法#3: 迭代多个列: 假设我们需要迭代多个列。为此，我们可以从 dataframe 中选择一个以上的列，并对其进行迭代。 代码:
Python 3
import pandas as pd
# List of Tuples
students = [('Ankit', 22, 'A'),
('Swapnil', 22, 'B'),
('Priya', 22, 'B'),
('Shivangi', 22, 'B'),
]
# Create a DataFrame object
stu_df = pd.DataFrame(students, columns =['Name', 'Age', 'Section'],
index =['1', '2', '3', '4'])
# Iterate over two given columns
# only from the dataframe
for column in stu_df[['Name', 'Section']]:
# Select column contents by column
# name using [] operator
columnSeriesObj = stu_df[column]
print('Column Name : ', column)
print('Column Contents : ', columnSeriesObj.values)
输出:
方法#4: 以相反的顺序迭代列: 我们也可以以相反的顺序迭代列。 代码:
Python 3
import pandas as pd
# List of Tuples
students = [('Ankit', 22, 'A'),
('Swapnil', 22, 'B'),
('Priya', 22, 'B'),
('Shivangi', 22, 'B'),
]
# Create a DataFrame object
stu_df = pd.DataFrame(students, columns =['Name', 'Age', 'Section'],
index =['1', '2', '3', '4'])
# Iterate over the sequence of column names
# in reverse order
for column in reversed(stu_df.columns):
# Select column contents by column
# name using [] operator
columnSeriesObj = stu_df[column]
print('Column Name : ', column)
print('Column Contents : ', columnSeriesObj.values)
输出:
方法#5: 使用索引(【iloc】): 要按索引迭代数据框的列，我们可以迭代一个范围，即从 0 到最大列数，对于每个索引，我们可以使用 iloc[]选择列的内容。 代码:
Python 3
import pandas as pd
# List of Tuples
students = [('Ankit', 22, 'A'),
('Swapnil', 22, 'B'),
('Priya', 22, 'B'),
('Shivangi', 22, 'B'),
]
# Create a DataFrame object
stu_df = pd.DataFrame(students, columns =['Name', 'Age', 'Section'],
index =['1', '2', '3', '4'])
# Iterate over the index range from
# 0 to max number of columns in dataframe
for index in range(stu_df.shape[1]):
print('Column Number : ', index)
# Select column by index position using iloc[]
columnSeriesObj = stu_df.iloc[:, index]
print('Column Contents : ', columnSeriesObj.values)
输出:
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