获取包含给定子字符串的 Pandas DataFrame 中的所有行

让我们看看如何借助不同的示例获取包含给定子字符串的 Pandas DataFrame 中的所有行。

代码 #1：检查位置列中的值 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, "\n")
  
print("Check PG values in Position column:\n")
df1 = df['Position'].str.contains("PG")
print(df1)

输出：

但是这个结果似乎不是很有帮助，因为它返回带有索引的布尔值。让我们看看我们是否可以做得更好。

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 College 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：过滤行检查团队名称包含'波士顿和位置必须是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：过滤行检查 Position 是否包含 PG，College 必须包含类似 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：