How to Slice Columns in pandas DataFrame

Use DataFrame.loc[] and DataFrame.iloc[] to slice the columns in pandas DataFrame where loc[] is used with column labels/names and iloc[] is used with column index/position. You can also use these operators to select rows from pandas DataFrame

Pandas DataFrame is a two-dimensional tabular data structure with labeled axes. i.e. columns and rows. Taking column slices of DataFrame results in a new DataFrame containing only specified columns from the original DataFrame.

In this article, I will explain how to slice/take or select a subset of a DataFrame by column labels, certain positions of the column, and by range e.t.c with examples.

1. Quick Examples of Column-Slices of Pandas DataFrame

If you are in a hurry, below are some quick examples of how to take columns slices of pandas DataFrame.


# Below are quick example
# Using loc[] to take column slices
# Slice selected multiple columns
df2=df.loc[:, ["Courses","Fee","Duration"]]

# Slice random selected columns
df2=df.loc[:, ["Courses","Fee","Discount"]]

# Slice columns by range
df2=df.loc[:,'Fee':'Discount']
df2=df.loc[:,'Duration':]
df2=df.loc[:,:'Duration']

# slice every alternate column
df2 = df.loc[:,::2]

# Using iloc[] to take column slices
# Slice by selected column position
df2 = df.iloc[:,[1,3,4]]

# Slice between indexes 1 and 4 (2,3,4)
df2 = df.iloc[:,1:4]

# Slice From 3rd to end
df2 = df.iloc[:,2:]

# Slice First Two Columns
df2 = df.iloc[:,:2]

Now, let’s create a DataFrame with a few rows and columns and execute some examples of how to slice columns in pandas. 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(df)

Yields below output.


   Courses    Fee Duration  Discount   Tutor
0    Spark  20000   30days      1000  Michel
1  PySpark  25000   40days      2300     Sam

2. Using Pandas.DataFrame.loc[] – Slice Columns by Names or Labels

By using pandas.DataFrame.loc[] you can slice columns by names or labels. To slice the columns, the syntax is df.loc[:,start:stop:step]; where start is the name of the first column to take, stop is the name of the last column to take, and step as the number of indices to advance after each extraction; for example, you can select alternate columns. Or, use the syntax: [:,[labels]] with labels as a list of column names to take.


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

2.1 Slice DataFrame Columns by Labels

To slice DataFrame columns by labels or names, all you need is to provide the multiple labels you wanted to slice as a list. Here we use the list of labels instead of the start:stop:step approach.


# Slice Columns by labels
df.loc[:, ["Courses","Fee","Duration"]]
#Output
#   Courses    Fee Duration
#0    Spark  20000   30days
#1  PySpark  25000   40days

2.2 Slice Certain Selective Columns in pandas

Sometimes you may want to select random certain columns from pandas DataFrame, you can do this by passing selected column names/labels as a list.


# Slice by Certain Columns
df.loc[:, ["Courses","Fee","Discount"]]
#Output
#   Courses    Fee  Discount
#0    Spark  20000      1000
#1  PySpark  25000      2300

2.3 Slice DataFrame Columns by Range

When you wanted to slice a DataFrame by the range of columns, provide start and stop column names.

  • By not providing a start column, loc[] selects from the beginning.
  • By not providing stop, loc[] selects all columns from the start label.
  • Providing both start and stop, selects all columns in between.

# Slice all columns between Fee an Discount columns
df2 = df.loc[:,'Fee':'Discount']
#Output
#     Fee Duration  Discount
#0  20000   30days      1000
#1  25000   40days      2300

# Slice start from 'Duration' column
df2 = df.loc[:,'Duration':]
#Output
#  Duration  Discount   Tutor
#0   30days      1000  Michel
#1   40days      2300     Sam

# Slice Start from beginning and end at 'Duration' column
df2 = df.loc[:,:'Duration']
#Output
#   Courses    Fee Duration
#0    Spark  20000   30days
#1  PySpark  25000   40days

2.4 Select Every Alternate Column

Using loc[], you can also slice columns by selecting every other column from pandas DataFrame.


# Slice every alternate column
df2 = df.loc[:,::2]
#Output
#   Courses Duration   Tutor
#0    Spark   30days  Michel
#1  PySpark   40days     Sam

3. Pandas DataFrame.iloc[] – Column Slices by Index or Position

By using pandas.DataFrame.iloc[] you can slice DataFrame by column position/index. ; Remember index starts from 0. You can use pandas.DataFrame.iloc[] with the syntax [:,start:stop:step] where start indicates the index of the first column to take, stop indicates the index of the last column to take, and step indicates the number of indices to advance after each extraction. Or, use the syntax: [:,[indices]] with indices as a list of column indices to take.

3.1. Slice Columns by Index Position

We are going to use columns by index position, and retrieve slices of DataFrame. Below example retrieves "Fee","Discount" and "Duration" slices of column DataFrame.


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

#Output
#     Fee  Discount   Tutor
#0  20000      1000  Michel
#1  25000      2300     Sam

3.2 Column Slices by Position Range

Like slices by column labels, you can also slice a DataFrame by a range of positions.


# Slice between indexes 1 and 4 (2,3,4)
print(df.iloc[:,1:4])
#     Fee Duration  Discount
#0  20000   30days      1000
#1  25000   40days      2300

# Slice From 3rd to end
print(df.iloc[:,2:])
#  Duration  Discount   Tutor
#0   30days      1000  Michel
#1   40days      2300     Sam

# Slice First Two Columns
print(df.iloc[:,:2])
#   Courses    Fee
#0    Spark  20000
#1  PySpark  25000

To get the last column use df.iloc[:,-1:] and to get just first column df.iloc[:,:1]

4. Complete Example To Take Column-Slices From DataFrame

Below is a complete example of how to take column slices from pandas DataFrame


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)

# Slice selected multiple columns
print(df.loc[:, ["Courses","Fee","Duration"]])

# Slice random selected columns
print(df.loc[:, ["Courses","Fee","Discount"]])

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

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

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

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

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

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

Conclusion

In this article, you have learned how to take column-slices of pandas DataFrame using DataFrame.loc[], and DataFrame.iloc[] function. loc[] is used with labels

Happy Learning !!

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How to Slice Columns in pandas DataFrame