获取包含给定子字符串的Pandas DataFrame中的所有行

 

 

让我们看看如何在不同示例的帮助下获取包含给定子字符串的Pandas DataFrame中的所有行。

代码1：检查'Position'列中的值PG

# importing pandas 
import pandas as pd 

# Creating the dataframe with dict of lists 
df = pd.DataFrame({'Name': ['Geeks', 'Peter', 'James', 'Jack', 'Lisa'], 
                'Team': ['Boston', 'Boston', 'Boston', 'Chele', 'Barse'], 
                'Position': ['PG', 'PG', 'UG', 'PG', 'UG'], 
                'Number': [3, 4, 7, 11, 5], 
                'Age': [33, 25, 34, 35, 28], 
                'Height': ['6-2', '6-4', '5-9', '6-1', '5-8'], 
                'Weight': [89, 79, 113, 78, 84], 
                'College': ['MIT', 'MIT', 'MIT', 'Stanford', 'Stanford'], 
                'Salary': [99999, 99994, 89999, 78889, 87779]}, 
                index =['ind1', 'ind2', 'ind3', 'ind4', 'ind5']) 
print(df, "
") 

print("Check PG values in Position column:
") 
df1 = df['Position'].str.contains("PG") 
print(df1) 

输出：

但是这个结果似乎并没有太大帮助，因为它返回带有索引的布尔值。让我们看看是否可以做得更好。
 

代码2：获取满足条件的行

# importing pandas as pd 
import pandas as pd 

# Creating the dataframe with dict of lists 
df = pd.DataFrame({'Name': ['Geeks', 'Peter', 'James', 'Jack', 'Lisa'], 
                'Team': ['Boston', 'Boston', 'Boston', 'Chele', 'Barse'], 
                'Position': ['PG', 'PG', 'UG', 'PG', 'UG'], 
                'Number': [3, 4, 7, 11, 5], 
                'Age': [33, 25, 34, 35, 28], 
                'Height': ['6-2', '6-4', '5-9', '6-1', '5-8'], 
                'Weight': [89, 79, 113, 78, 84], 
                'College': ['MIT', 'MIT', 'MIT', 'Stanford', 'Stanford'], 
                'Salary': [99999, 99994, 89999, 78889, 87779]}, 
                index =['ind1', 'ind2', 'ind3', 'ind4', 'ind5']) 

df1 = df[df['Position'].str.contains("PG")] 
print(df1) 

输出：

代码3：过滤Team包含‘Boston’或大学包含“ MIT”的所有行。

# importing pandas 
import pandas as pd 

# Creating the dataframe with dict of lists 
df = pd.DataFrame({'Name': ['Geeks', 'Peter', 'James', 'Jack', 'Lisa'], 
                'Team': ['Boston', 'Boston', 'Boston', 'Chele', 'Barse'], 
                'Position': ['PG', 'PG', 'UG', 'PG', 'UG'], 
                'Number': [3, 4, 7, 11, 5], 
                'Age': [33, 25, 34, 35, 28], 
                'Height': ['6-2', '6-4', '5-9', '6-1', '5-8'], 
                'Weight': [89, 79, 113, 78, 84], 
                'College': ['MIT', 'MIT', 'MIT', 'Stanford', 'Stanford'], 
                'Salary': [99999, 99994, 89999, 78889, 87779]}, 
                index =['ind1', 'ind2', 'ind3', 'ind4', 'ind5']) 


df1 = df[df['Team'].str.contains("Boston") | df['College'].str.contains('MIT')] 
print(df1) 

输出：

 
代码4：筛选行，检查Team名称是否包含‘Boston’，Position必须为PG。

# importing pandas module 
import pandas as pd 
    
# making data frame 
df = pd.read_csv("https://media.geeksforgeeks.org/wp-content/uploads/nba.csv") 


df1 = df[df['Team'].str.contains('Boston') & df['Position'].str.contains('PG')] 
df1 

输出：

 

代码5： Filter rows checking Position contains PG and College must contains like UC

# importing pandas module 
import pandas as pd 
    
# making data frame 
df = pd.read_csv("https://media.geeksforgeeks.org/wp-content/uploads/nba.csv") 


df1 = df[df['Position'].str.contains("PG") & df['College'].str.contains('UC')] 
df1 

输出：