Pandas Select Columns by Name or Index

In Pandas, selecting columns by name or index allows you to access specific columns in a DataFrame based on their labels (names) or positions (indices). Use loc[] & iloc[] to select a single column or multiple columns from pandas DataFrame by column names/label or index position respectively. Also, refer to a related article how to get cell value from pandas DataFrame.

In this article, I will explain how to select single or multiple columns from a DataFrame using different methods such as column labels, index, positions, and ranges.

Key Points –

• Pandas allow selecting columns from a DataFrame by their names using square brackets notation or the .loc[] accessor.
• The .loc[] accessor allows for more explicit selection, accepting row and column labels or boolean arrays.
• Alternatively, you can use the .iloc[] accessor to select columns by their integer index positions.
• For selecting the last column, use df.iloc[:,-1:], and for the first column, use df.iloc[:,:1].
• Understanding both column name and index-based selection is essential for efficient data manipulation with Pandas.

Quick Examples of Select Columns by Name or Index

If you are in a hurry, below are some quick examples of how to select columns by name or index in Pandas DataFrame.

# Quick examples of select columns by name or index

# Example 1: By using df[] notation
df2 = df[["Courses","Fee","Duration"]] # select multile columns

# Example 2: Using loc[] to take column slices
df2 = df.loc[:, ["Courses","Fee","Duration"]] # Selecte multiple columns
df2 = df.loc[:, ["Courses","Fee","Discount"]] # Select Random columns
df2 = df.loc[:,'Fee':'Discount'] # Select columns between two columns
df2 = df.loc[:,'Duration':]  # Select columns by range
df2 = df.loc[:,:'Duration']  # Select columns by range
df2 = df.loc[:,::2]          # Select every alternate column

# Example 3: Using iloc[] to select column by Index
df2 = df.iloc[:,[1,3,4]] # Select columns by Index
df2 = df.iloc[:,1:4] # Select between indexes 1 and 4 (2,3,4)
df2 = df.iloc[:,2:] # Select From 3rd to end
df2 = df.iloc[:,:2] # Select First Two Columns

Let’s create a DataFrame with a few rows and columns, execute these examples, and validate results. Our DataFrame contains column names Courses, Fee, Duration, and Discount.

import pandas as pd
technologies = {
    'Courses':["Spark","PySpark"],
    'Fee' :[20000,25000],
    'Duration':['30days','40days'],
    'Discount':[1000,2300]
              }
df = pd.DataFrame(technologies)
print("Create DataFrame:\n", df)

Yields below output.

Using loc[] to Select Columns by Name

The df[] and DataFrame.loc[] methods in Pandas provide convenient ways to select multiple columns by names or labels, you can use the syntax [:, start:stop:step] to define the range of columns to include, where the start is the index where the slice starts (inclusive), stop is the index where the slice ends (exclusive), and step is the step size between elements. Another syntax supported by pandas.DataFrame.loc[] is [:, [labels]], where you provide a list of column names as labels.

# loc[] syntax to slice columns
df.loc[:,start:stop:step]

Select DataFrame Columns by Name

To select DataFrame columns by name, you can directly specify the column names within square brackets []. Here, df[['Courses', 'Fee', 'Duration']] select only the Courses, Fee, and Duration columns from the DataFrame df.

# Select Columns by labels
df2 = df[["Courses","Fee","Duration"]]
print("Select columns by labels:\n", df2)

Yields below output.

Select Columns by Index in Multiple Columns

To select multiple columns using df.loc[], you specify both row and column labels. If you want to select all rows and specific columns, you can use : to select all rows and provide a list of column labels. Note that loc[] also supports multiple conditions when selecting rows based on column values.

# Select multiple columns
df2 = df.loc[:, ["Courses","Fee","Discount"]]
print("Select multiple columns by labels:\n", df2)

# Output:
# Select multiple columns by labels:
#   Courses    Fee  Discount
# 0    Spark  20000      1000
# 1  PySpark  25000      2300

In the above example, df.loc[:, ["Courses", "Fee", "Discount"]] selects all rows (:) and the columns labeled Courses, Fee, and Discount from the DataFrame df.

Select Columns Based on Label Indexing

When you want to select columns based on label Indexes, provide start and stop indexes.

• If you don’t provide a start index, iloc[] selects from the first column.
• By not providing stop, iloc[] select all columns from the start index to the last column.
• Providing both start and stop indexes selects all columns in between, inclusive of the start index but exclusive of the stop index.

# Select all columns between Fee an Discount columns
df2 = df.loc[:,'Fee':'Discount']
print("Select columns by labels:\n", df2)

# Output:
# Select columns by labels:
#     Fee Duration  Discount
# 0  20000   30days      1000
# 1  25000   40days      2300

# Select from 'Duration' column
df2 = df.loc[:,'Duration':]
print("Select columns by labels:\n", df2)

# Output
# Select columns by labels:
#  Duration  Discount   Tutor
# 0   30days      1000  Michel
# 1   40days      2300     Sam

# Select from beginning and end at 'Duration' column
df2 = df.loc[:,:'Duration']
print("Select columns by labels:\n", df2)

# Output
# Select columns by labels:
#   Courses    Fee Duration
# 0    Spark  20000   30days
# 1  PySpark  25000   40days

Select Every Alternate Column

You can select every alternate column from a DataFrame, you can use the iloc[] accessor with a step size of 2.

# Select every alternate column
df2 = df.loc[:,::2]
print("Select columns by labels:\n", df2)

# Output:
# Select columns by labels:
#   Courses Duration   Tutor
# 0    Spark   30days  Michel
# 1  PySpark   40days     Sam

This code effectively selects every alternate column, starting from the first column, which results in selecting Courses and Duration.

Pandas iloc[] to Select Column by Index or Position

By using pandas.DataFrame.iloc[], to select multiple columns from a DataFrame by their positional indices. You can use the syntax [:, start:stop:step] to define the range of columns to include, where the start is the index where the slice starts (inclusive), stop is the index where the slice ends (exclusive), and step is the step size between elements. Or, you can use the syntax [:, [indices]] with iloc[], where you provide a list of column names as labels.

Select Columns by Index Position

To select multiple columns from a DataFrame by their index positions, you can use the iloc[] accessor. For instance, retrieves Fee, Discount and Duration and returns a new DataFrame with the columns selected.

# Select columns by position
df2 = df.iloc[:,[1,3,4]]
print("Selec columns by position:\n", df2)

# Output:
# Selec columns by position:
#     Fee  Discount   Tutor
# 0  20000      1000  Michel
# 1  25000      2300     Sam

Select Columns by Position Range

You can also slice a DataFrame by a range of positions. For instance, select columns by position range using the .iloc[] accessor in Pandas. It selects columns with positions 1 through 3 (exclusive of position 4) from the DataFrame df and assigns them to df2.

# Select between indexes 1 and 4 (2,3,4)
df2 = df.iloc[:,1:4]
print("Select columns by position:\n", df2)

# OUtput:
# Selec columns by position:
#     Fee Duration  Discount
# 0  20000   30days      1000
# 1  25000   40days      2300

# Select From 3rd to end
df2 = df.iloc[:,2:]
print("Select columns by position:\n", df2)

# Output:
# Selec columns by position:
#  Duration  Discount   Tutor
# 0   30days      1000  Michel
# 1   40days      2300     Sam

# Select First Two Columns
df2 = df.iloc[:,:2]
print("Selec columns by position:\n", df2))

# Output:
# Selec columns by position:
#   Courses    Fee
# 0    Spark  20000
# 1  PySpark  25000

To retrieve the last column of a DataFrame, you can use df.iloc[:,-1:], and to obtain just the first column, you can use df.iloc[:,:1].

Complete Example

import pandas as pd
technologies = {
    'Courses':["Spark","PySpark"],
    'Fee' :[20000,25000],
    'Duration':['30days','40days'],
    'Discount':[1000,2300],
    'Tutor':['Michel','Sam']
              }
df = pd.DataFrame(technologies)
print(df)

# Select multiple columns
print(df[["Courses","Fee","Duration"]])

# Select Random columns
print(df.loc[:, ["Courses","Fee","Discount"]])

# Select columns by range
print(df.loc[:,'Fee':'Discount']) 
print(df.loc[:,'Duration':])
print(df.loc[:,:'Duration'])

# Select every alternate column
print(df.loc[:,::2])

# Selected by column position
print(df.iloc[:,[1,3,4]])

# Select between indexes 1 and 4 (2,3,4)
print(df.iloc[:,1:4])

# Select From 3rd to end
print(df.iloc[:,2:])

# Select First Two Columns
print(df.iloc[:,:2])

Frequently Asked Questions

Conclusion

In this article, you have learned the pandas select columns by name or index using DataFrame.loc[], and DataFrame.iloc[] properties. To understand the similarities and differences between these two refer to pandas loc[] vs iloc[] with examples.

Happy Learning !!

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• How to Combine Two Series into pandas DataFrame
• Install pandas on Windows Step-by-Step
• Convert Index to Column in Pandas DataFrame
• Pandas Select DataFrame Rows Between Two Dates
• Pandas Select Multiple Columns in DataFrame
• Set Order of Columns in Pandas DataFrame
• Select Pandas Columns Based on Condition
• Pandas Create Conditional Column in DataFrame 
• Replace NaN Values with Zeroes in a Column of a Pandas DataFrame

References

• https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.loc.html