Pandas DataFrame Slicing – Vista Academy

Pandas DataFrame slicing is a process of extracting specific rows, columns, or subsets of data based on both position and labels. DataFrame slicing is a common operation while working with large datasets, and it is similar to Python lists and NumPy ndarrays. DataFrame slicing uses the [] operator and specific slicing attributes like .iloc[] and .loc[] to retrieve data efficiently.

Introduction to Pandas DataFrame Slicing

Pandas DataFrame slicing is performed using two main attributes, which are:

• .iloc[]: For slicing based on position (integer-based indexing).
• .loc[]: For slicing based on labels (index labels or column labels).

Slicing a DataFrame by Position

The Pandas DataFrame.iloc[] attribute is used to slice a DataFrame based on the integer position (i.e., integer-based indexing) of rows and columns.

import pandas as pd

# Create a Pandas DataFrame
df = pd.DataFrame([["a","b"], ["c","d"], ["e","f"], ["g","h"]], columns=['col1', 'col2'])

# Display the DataFrame
print("Input DataFrame:")
print(df)

# Slice rows based on position
result = df.iloc[1:3, :]
print("Output:")
print(result)

Output:

Input DataFrame:
  col1 col2
0    a    b
1    c    d
2    e    f
3    g    h

Output:
  col1 col2
1    c    d
2    e    f

Slicing a DataFrame by Label

The Pandas DataFrame.loc[] attribute is used to slice a DataFrame based on the labels of rows and columns.

import pandas as pd

# Create a DataFrame with labeled indices
df = pd.DataFrame([["a","b"], ["c","d"], ["e","f"], ["g","h"]], columns=['col1', 'col2'], index=['r1', 'r2', 'r3', 'r4'])

# Display the DataFrame
print("Original DataFrame:")
print(df)

# Slice rows and columns by label
result = df.loc['r1':'r3', 'col1']
print("Output:")
print(result)

Output:

Original DataFrame:
   col1 col2
r1    a    b
r2    c    d
r3    e    f
r4    g    h

Output:
r1    a
r2    c
r3    e
Name: col1, dtype: object

DataFrame Column Slicing

Example: Column Slicing using iloc[]

import pandas as pd

data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}
df = pd.DataFrame(data)

# Slice a single column
col_A = df.iloc[:, 0]
print("Slicing a single column A using iloc[]:")
print(col_A)

# Slice multiple columns
cols_AB = df.iloc[:, 0:2]
print("Slicing multiple columns A and B using iloc[]:")
print(cols_AB)

Output:

Slicing a single column A using iloc[]:
0    1
1    2
2    3
Name: A, dtype: int64

Slicing multiple columns A and B using iloc[]:
   A  B
0  1  4
1  2  5
2  3  6

Example: Column Slicing Using loc[]

import pandas as pd

data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}
df = pd.DataFrame(data)

# Slice a single column by label
col_A = df.loc[:, 'A']
print("Slicing a single column A using loc[]:")
print(col_A)

# Slice multiple columns by label
cols_AB = df.loc[:, 'A':'B']
print("Slicing Multiple columns A and B using loc[]:")
print(cols_AB)

Output:

Slicing a single column A using loc[]:
0    1
1    2
2    3
Name: A, dtype: int64

Slicing Multiple columns A and B using loc[]:
   A  B
0  1  4
1  2  5
2  3  6

Modifying Values After Slicing

After slicing a DataFrame, you can modify the sliced values directly by assigning new values to the selected elements.

import pandas as pd

# Create a DataFrame
df = pd.DataFrame([["a", "b"], ["c", "d"], ["e", "f"], ["g", "h"]], 
                  columns=['col1', 'col2'])

# Display the Original DataFrame
print("Original DataFrame:", df, sep='\n')

# Modify a subset of the DataFrame using iloc
df.iloc[1:3, 0] = ['x', 'y']

# Display the modified DataFrame
print('Modified DataFrame:',df, sep='\n')

Output:

Original DataFrame:
  col1 col2
0    a    b
1    c    d
2    e    f
3    g    h

Modified DataFrame:
  col1 col2
0    a    b
1    x    d
2    y    f
3    g    h