迭代熊猫数据框中的行和列
原文:https://www . geesforgeks . org/iterating-over-row-and-columns-in-pandas-data frame/
迭代是一个通用术语，指的是一个接一个地获取事物的每一项。熊猫数据帧由行和列组成，所以为了迭代数据帧，我们必须像字典一样迭代数据帧。在字典中，我们迭代对象的键，就像在 dataframe 中迭代一样。
本文中，我们是用“nba.csv”文件下载的 csv，点击这里。 在熊猫数据框中，我们可以用两种方式迭代一个元素:
- 遍历行
- 在列上迭代
遍历行:
为了遍历行，我们可以使用三个函数 iteritems()，iterrows()，over 元组()。这三个函数将有助于对行进行迭代。
使用 iterrows()对行进行迭代
为了遍历行，我们应用 iterrows()函数，该函数返回每个索引值以及包含每行数据的序列。
代码#1:
Python 3
# importing pandas as pd
import pandas as pd
# dictionary of lists
dict = {'name':["aparna", "pankaj", "sudhir", "Geeku"],
'degree': ["MBA", "BCA", "M.Tech", "MBA"],
'score':[90, 40, 80, 98]}
# creating a dataframe from a dictionary
df = pd.DataFrame(dict)
print(df)
现在我们应用 iterrows()函数来获取行的每个元素。
Python 3
# importing pandas as pd
import pandas as pd
# dictionary of lists
dict = {'name':["aparna", "pankaj", "sudhir", "Geeku"],
'degree': ["MBA", "BCA", "M.Tech", "MBA"],
'score':[90, 40, 80, 98]}
# creating a dataframe from a dictionary
df = pd.DataFrame(dict)
# iterating over rows using iterrows() function
for i, j in df.iterrows():
print(i, j)
print()
输出:
代码#2:
计算机编程语言
# importing pandas module
import pandas as pd
# making data frame from csv file
data = pd.read_csv("nba.csv")
# for data visualization we filter first 3 datasets
data.head(3)
现在我们应用一个 iterrows 来获取 dataframe 中每一行的柠檬
计算机编程语言
# importing pandas module
import pandas as pd
# making data frame from csv file
data = pd.read_csv("nba.csv")
for i, j in data.iterrows():
print(i, j)
print()
输出:
使用 over()对行进行迭代
为了遍历行，我们使用 iteritems()函数该函数遍历每个列作为键，以标签作为键的值对，以及作为 Series 对象的列值。
代码#1:
Python 3
# importing pandas as pd
import pandas as pd
# dictionary of lists
dict = {'name':["aparna", "pankaj", "sudhir", "Geeku"],
'degree': ["MBA", "BCA", "M.Tech", "MBA"],
'score':[90, 40, 80, 98]}
# creating a dataframe from a dictionary
df = pd.DataFrame(dict)
print(df)
现在我们应用 iteritems()函数来检索 dataframe 的行。
Python 3
# importing pandas as pd
import pandas as pd
# dictionary of lists
dict = {'name':["aparna", "pankaj", "sudhir", "Geeku"],
'degree': ["MBA", "BCA", "M.Tech", "MBA"],
'score':[90, 40, 80, 98]}
# creating a dataframe from a dictionary
df = pd.DataFrame(dict)
# using iteritems() function to retrieve rows
for key, value in df.iteritems():
print(key, value)
print()
输出:
代码#2:
计算机编程语言
# importing pandas module
import pandas as pd
# making data frame from csv file
data = pd.read_csv("nba.csv")
# for data visualization we filter first 3 datasets
data.head(3)
输出:
现在，我们应用 item()从数据框中检索行
计算机编程语言
# importing pandas module
import pandas as pd
# making data frame from csv file
data = pd.read_csv("nba.csv")
for key, value in data.iteritems():
print(key, value)
print()
输出:
使用 over 元组对行进行迭代()
为了遍历行，我们应用一个函数 itertuples()，这个函数为数据帧中的每一行返回一个 tuple。元组的第一个元素将是行的对应索引值，而其余的值是行值。
代码#1:
Python 3
# importing pandas as pd
import pandas as pd
# dictionary of lists
dict = {'name':["aparna", "pankaj", "sudhir", "Geeku"],
'degree': ["MBA", "BCA", "M.Tech", "MBA"],
'score':[90, 40, 80, 98]}
# creating a dataframe from a dictionary
df = pd.DataFrame(dict)
print(df)
现在，我们应用 ITER 元组()函数来获取每行的元组
Python 3
# importing pandas as pd
import pandas as pd
# dictionary of lists
dict = {'name':["aparna", "pankaj", "sudhir", "Geeku"],
'degree': ["MBA", "BCA", "M.Tech", "MBA"],
'score':[90, 40, 80, 98]}
# creating a dataframe from dictionary
df = pd.DataFrame(dict)
# using a itertuples()
for i in df.itertuples():
print(i)
输出:
代码#2:
计算机编程语言
# importing pandas module
import pandas as pd
# making data frame from csv file
data = pd.read_csv("nba.csv")
# for data visualization we filter first 3 datasets
data.head(3)
现在，我们应用 ITER 元组()来获取每一行的一个实例
计算机编程语言
# importing pandas module
import pandas as pd
# making data frame from csv file
data = pd.read_csv("nba.csv")
for i in data.itertuples():
print(i)
输出:
在列上迭代:
为了遍历列，我们需要创建一个 dataframe 列列表，然后遍历该列表以拉出 dataframe 列。
代码#1:
Python 3
# importing pandas as pd
import pandas as pd
# dictionary of lists
dict = {'name':["aparna", "pankaj", "sudhir", "Geeku"],
'degree': ["MBA", "BCA", "M.Tech", "MBA"],
'score':[90, 40, 80, 98]}
# creating a dataframe from a dictionary
df = pd.DataFrame(dict)
print(df)
现在我们遍历列为了遍历列，我们首先创建一个 dataframe 列的列表，然后遍历列表。
计算机编程语言
# creating a list of dataframe columns
columns = list(df)
for i in columns:
# printing the third element of the column
print (df[i][2])
输出:
代码#2:
计算机编程语言
# importing pandas module
import pandas as pd
# making data frame from csv file
data = pd.read_csv("nba.csv")
# for data visualization we filter first 3 datasets
col = data.head(3)
col
现在我们迭代 CSV 文件中的列，为了迭代列，我们创建一个数据框列列表，并迭代列表
计算机编程语言
# creating a list of dataframe columns
clmn = list(col)
for i in clmn:
# printing a third element of column
print(col[i][2])
输出:
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