Выберите строки и столбцы по имени или индексу в Pandas DataFrame, используя [], loc & iloc

Опубликовано: 27 Марта, 2022

Indexing in Pandas means selecting rows and columns of data from a Dataframe. It can be selecting all the rows and the particular number of columns, a particular number of rows, and all the columns or a particular number of rows and columns each. Indexing is also known as Subset selection.
Let’s create a simple dataframe with a list of tuples, say column names are: ‘Name’, ‘Age’, ‘City’ and ‘Salary’.

# import pandas
import pandas as pd
  
# List of Tuples
employees = [("Stuti", 28, "Varanasi", 20000),
            ("Saumya", 32, "Delhi", 25000),
            ("Aaditya", 25, "Mumbai", 40000),
            ("Saumya", 32, "Delhi", 35000),
            ("Saumya", 32, "Delhi", 30000),
            ("Saumya", 32, "Mumbai", 20000),
            ("Aaditya", 40, "Dehradun", 24000),
            ("Seema", 32, "Delhi", 70000)
            ]
  
# Create a DataFrame object from list 
df = pd.DataFrame(employees, 
                columns =["Name", "Age", 
                         "City", "Salary"])
# Show the dataframe
df

Выход:

Метод 1: использование Dataframe. [].
[] используется для выбора столбца путем упоминания имени соответствующего столбца.

Example 1 : to select single column.
Code:

# import pandas
import pandas as pd
  
# List of Tuples
employees = [("Stuti", 28, "Varanasi", 20000),
            ("Saumya", 32, "Delhi", 25000),
            ("Aaditya", 25, "Mumbai", 40000),
            ("Saumya", 32, "Delhi", 35000),
            ("Saumya", 32, "Delhi", 30000),
            ("Saumya", 32, "Mumbai", 20000),
            ("Aaditya", 40, "Dehradun", 24000),
            ("Seema", 32, "Delhi", 70000)
            ]
  
# Create a DataFrame object from list 
df = pd.DataFrame(employees, 
                columns =["Name", "Age", 
                         "City", "Salary"])
  
# Using the operator [] 
# to select a column
result = df["City"]
  
# Show the dataframe
result

Выход:

Example 2: to select multiple columns.
Code:

# import pandas
import pandas as pd
  
# List of Tuples
employees = [("Stuti", 28, "Varanasi", 20000),
            ("Saumya", 32, "Delhi", 25000),
            ("Aaditya", 25, "Mumbai", 40000),
            ("Saumya", 32, "Delhi", 35000),
            ("Saumya", 32, "Delhi", 30000),
            ("Saumya", 32, "Mumbai", 20000),
            ("Aaditya", 40, "Dehradun", 24000),
            ("Seema", 32, "Delhi", 70000)
            ]
  
# Create a DataFrame object from list 
df = pd.DataFrame(employees, 
                columns =["Name", "Age",
                        "City", "Salary"])
  
# Using the operator [] to 
# select multiple columns
result = df[["Name", "Age", "Salary"]]
  
# Show the dataframe
result

Выход:

Method 2: Using Dataframe.loc[ ].
.loc[] the function selects the data by labels of rows or columns. It can select a subset of rows and columns. There are many ways to use this function.
Example 1: To select single row.
Code:

# import pandas
import pandas as pd
  
# List of Tuples
employees = [("Stuti", 28, "Varanasi", 20000),
            ("Saumya", 32, "Delhi", 25000),
            ("Aaditya", 25, "Mumbai", 40000),
            ("Saumya", 32, "Delhi", 35000),
            ("Saumya", 32, "Delhi", 30000),
            ("Saumya", 32, "Mumbai", 20000),
            ("Aaditya", 40, "Dehradun", 24000),
            ("Seema", 32, "Delhi", 70000)
            ]
  
# Create a DataFrame object from list 
df = pd.DataFrame(employees,
                 columns =["Name", "Age",
                  "City", "Salary"])
  
# Set "Name" column as index 
# on a Dataframe
df.set_index("Name", inplace = True)
  
# Using the operator .loc[]
# to select single row
result = df.loc["Stuti"]
  
# Show the dataframe
result

Выход:

Example 2: To select multiple rows.
Code:

# import pandas
import pandas as pd
  
# List of Tuples
employees = [("Stuti", 28, "Varanasi", 20000),
            ("Saumya", 32, "Delhi", 25000),
            ("Aaditya", 25, "Mumbai", 40000),
            ("Saumya", 32, "Delhi", 35000),
            ("Saumya", 32, "Delhi", 30000),
            ("Saumya", 32, "Mumbai", 20000),
            ("Aaditya", 40, "Dehradun", 24000),
            ("Seema", 32, "Delhi", 70000)
            ]
  
# Create a DataFrame object from list 
df = pd.DataFrame(employees, 
                  columns =["Name", "Age",
                   "City", "Salary"])
  
# Set index on a Dataframe
df.set_index("Name", 
              inplace = True)
  
# Using the operator .loc[]
# to select multiple rows
result = df.loc[["Stuti", "Seema"]]
  
# Show the dataframe
result

Выход:

Пример 3: Чтобы выбрать несколько строк и определенных столбцов.

 Синтаксис: Dataframe.loc [["строка1", "строка2" ...], ["столбец1", "столбец2", "столбец3" ...]]

Код:

# import pandas
import pandas as pd
  
# List of Tuples
employees = [("Stuti", 28, "Varanasi", 20000),
            ("Saumya", 32, "Delhi", 25000),
            ("Aaditya", 25, "Mumbai", 40000),
            ("Saumya", 32, "Delhi", 35000),
            ("Saumya", 32, "Delhi", 30000),
            ("Saumya", 32, "Mumbai", 20000),
            ("Aaditya", 40, "Dehradun", 24000),
            ("Seema", 32, "Delhi", 70000)
            ]
  
# Create a DataFrame object from list 
df = pd.DataFrame(employees, 
                 columns =["Name", "Age",
                  "City", "Salary"])
  
# Set "Name" column as index 
# on a Dataframe
df.set_index("Name", inplace = True)
  
# Using the operator .loc[] to 
# select multiple rows with some
# particular columns
result = df.loc[["Stuti", "Seema"],
               ["City", "Salary"]]
  
# Show the dataframe
result

Выход:

Пример 4: Выбрать все строки с некоторыми конкретными столбцами. Мы используем одиночное двоеточие [:] для выбора всех строк и списка столбцов, которые мы хотим выбрать, как показано ниже:

 Синтаксис: Dataframe.loc [[:, ["столбец1", "столбец2", "столбец3"]]

Code:

# import pandas
import pandas as pd
  
# List of Tuples
employees = [("Stuti", 28, "Varanasi", 20000),
            ("Saumya", 32, "Delhi", 25000),
            ("Aaditya", 25, "Mumbai", 40000),
            ("Saumya", 32, "Delhi", 35000),
            ("Saumya", 32, "Delhi", 30000),
            ("Saumya", 32, "Mumbai", 20000),
            ("Aaditya", 40, "Dehradun", 24000),
            ("Seema", 32, "Delhi", 70000)
            ]
  
# Creating a DataFrame object from list 
df = pd.DataFrame(employees,
                  columns =["Name", "Age", 
                  "City", "Salary"])
  
# Set "Name" column as index 
# on a Dataframe
df.set_index("Name", inplace = True)
  
# Using the operator .loc[] to
# select all the rows with 
# some particular columns
result = df.loc[:, ["City", "Salary"]]
  
# Show the dataframe
result

Выход:

Method 3: Using Dataframe.iloc[ ].
iloc[ ] is used for selection based on position. It is similar to loc[] indexer but it takes only integer values to make selections.
Example 1 : to select a single row.
Code:

# import pandas
import pandas as pd
  
# List of Tuples
employees = [("Stuti", 28, "Varanasi", 20000),
            ("Saumya", 32, "Delhi", 25000),
            ("Aaditya", 25, "Mumbai", 40000),
            ("Saumya", 32, "Delhi", 35000),
            ("Saumya", 32, "Delhi", 30000),
            ("Saumya", 32, "Mumbai", 20000),
            ("Aaditya", 40, "Dehradun", 24000),
            ("Seema", 32, "Delhi", 70000)
            ]
  
# Create a DataFrame object from list 
df = pd.DataFrame(employees, 
                  columns =["Name", "Age",
                   "City", "Salary"])
  
# Using the operator .iloc[]
# to select single row
result = df.iloc[2]
  
# Show the dataframe
result

Выход:

Example 2: to select multiple rows.
Code:

# import pandas
import pandas as pd
  
# List of Tuples
employees = [("Stuti", 28, "Varanasi", 20000),
            ("Saumya", 32, "Delhi", 25000),
            ("Aaditya", 25, "Mumbai", 40000),
            ("Saumya", 32, "Delhi", 35000),
            ("Saumya", 32, "Delhi", 30000),
            ("Saumya", 32, "Mumbai", 20000),
            ("Aaditya", 40, "Dehradun", 24000),
            ("Seema", 32, "Delhi", 70000)
            ]
  
# Create a DataFrame object from list 
df = pd.DataFrame(employees, 
                columns =["Name", "Age",
                "City", "Salary"])
  
# Using the operator .iloc[] 
# to select multiple rows
result = df.iloc[[2, 3, 5]]
  
# Show the dataframe
result

Выход:

Example 3: to select multiple rows with some particular columns.
Code:

# import pandas
import pandas as pd
  
# List of Tuples
employees = [("Stuti", 28, "Varanasi", 20000),
            ("Saumya", 32, "Delhi", 25000),
            ("Aaditya", 25, "Mumbai", 40000),
            ("Saumya", 32, "Delhi", 35000),
            ("Saumya", 32, "Delhi", 30000),
            ("Saumya", 32, "Mumbai", 20000),
            ("Aaditya", 40, "Dehradun", 24000),
            ("Seema", 32, "Delhi", 70000)
            ]
  
# Creating a DataFrame object from list 
df = pd.DataFrame(employees,
                  columns =["Name", "Age",
                  "City", "Salary"])
  
# Using the operator .iloc[] 
# to select multiple rows with
# some particular columns
result = df.iloc[[2, 3, 5],
                  [0,
Python

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