Common Pandas Operations with Examples

Popular Pandas Operations with Examples

If you want to use Pandas in your data science project, then learn followings common pandas operations:

1. Creating Data
2. Accessing Data
3. Data Selection
4. Data Manipulation
5. Data Aggregation and Summarization

Let's explore these operations with examples:

1. Creating Data

Let's create Series and DataFrame type of variables to apply various Pandas Operations on it.  

Creating a Series

import pandas as pd

# Prepare data variable

data = [1, 2, 3, 4, 5]

# Creating a Series

my_series = pd.Series(data)

# Print a Series

print(my_series)

Output:

0 1

1 2

2 3

3 4

4 5

dtype: int64

  

Creating a DataFrame

# Create dictionaries for each column

data = {

    'Name': ['Kuntal', 'Sunit', 'Tapan', 'Amit'],

    'Age': [40, 38, 42, 44],

    'Bank': ['SBI', 'HDFC', 'ICICI', 'KOTAK']

}

# Create a DataFrame

df = pd.DataFrame(data)

# Print the DataFrame

print(df)

Output:

Name Age Bank 0 Kuntal 40 SBI 1 Sunit 38 HDFC 2 Tapan 42 ICICI 3 Amit 44 KOTAK

2. Accessing Data

Popular way to access data are:

• .head() : To view the first few rows.
• .tail() : To view the last few rows.
• [ ] : Access columns or rows by labels or positions (similar to list indexing).

Examples:

# To view first 2 rows from DataFrame "df"

df.head(2)

Output:

Name Age Bank 0 Kuntal 40 SBI 1 Sunit 38 HDFC

# To print last 2 rows from DataFrame "df"

df.tail(2)

Output:

    Name    Age Bank

2   Tapan   42  ICICI

3   Amit    44  KOTAK

3. Data Selection

Use following functions for data selection.

• .loc[ ]: Select rows/columns based on labels.
• .iloc[ ]: Select rows/columns based on positions (zero-based indexing).
• Boolean filtering for conditional selection.

Examples:

print(df.loc[df['Age'] > 40])  # Select rows where 'Age' > 40

Output:

Name Age Bank 2 Tapan 42 ICICI 3 Amit 44 KOTAK

print(df.iloc[1:3])  # Select rows from index 1 (inclusive) to 3 (exclusive)

Output:

Name Age Bank 1 Sunit 38 HDFC 2 Tapan 42 ICICI

4. Data Manipulation

Following data manipulation operations can be done on data:

• Add, remove, or rename columns.
• Create new columns based on calculations.
• Handle missing values.

Example:

To rename a given column, write following code:

# To rename "Age" column with "Years Old" in "df" variable

df.rename(columns={'Age': 'Years Old'}, inplace=True)

print(df)

Output:

Name Years Old Bank 0 Kuntal 40 SBI 1 Sunit 38 HDFC 2 Tapan 42 ICICI 3 Amit 44 KOTAK

5. Data Aggregation and Summarization

• Calculate descriptive statistics (mean, median, standard deviation, etc.).
• Group data and perform aggregate operations (sum, count, average).

Example:

print(df.describe())  

Output:

Years Old count 4.000000 mean 41.000000 std 2.581989 min 38.000000 25% 39.500000 50% 41.000000 75% 42.500000 max 44.000000

where, 25%, 50% and 75% are Percentile,  it means how many of the values are less than the given percentile.