Data Science Pandas for Data Manipulation

Pandas is one of the most popular Python libraries for data manipulation and analysis. It provides powerful, flexible, and easy-to-use data structures like Series and DataFrames, which are essential tools for data scientists. In this tutorial, we will explore the basics of Pandas and how to use it for data manipulation, cleaning, and filtering.

1. Installing Pandas

If you haven’t installed Pandas yet, you can install it via pip:

pip install pandas

pip install pandas

2. Importing Pandas

Before using Pandas, you need to import it into your Python script:

import pandas as pd

import pandas as pd

3. Creating Pandas DataFrame

The primary data structure in Pandas is the DataFrame, which is essentially a table or 2D array with labeled axes (rows and columns). Here’s how to create a DataFrame from a dictionary:

data = {'Name': ['Alice', 'Bob', 'Charlie'],

'Age': [25, 30, 35],

'City': ['New York', 'Los Angeles', 'Chicago']}

df = pd.DataFrame(data)

print(df)

data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35], 'City': ['New York', 'Los Angeles', 'Chicago']} df = pd.DataFrame(data) print(df)

data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35],
        'City': ['New York', 'Los Angeles', 'Chicago']}

df = pd.DataFrame(data)
print(df)

Output:

      Name  Age         City
0    Alice   25     New York
1      Bob   30  Los Angeles
2  Charlie   35      Chicago

4. Viewing Data

Once you have a DataFrame, you can view the first few or last few rows using:

First few rows: df.head()

print(df.head()) # Output: First 5 rows

print(df.head())  # Output: First 5 rows

Last few rows: df.tail()

print(df.tail()) # Output: Last 5 rows

print(df.tail())  # Output: Last 5 rows

5. Accessing Data in DataFrame

DataFrames allow you to access data by column, row, or by both row and column:

Accessing a single column: Use df['column_name']

print(df['Name']) # Output: Column 'Name'

print(df['Name'])  # Output: Column 'Name'

Accessing multiple columns: Use df[['col1', 'col2']]

print(df[['Name', 'City']])

print(df[['Name', 'City']])

Accessing a row by index: Use df.iloc[row_index]

print(df.iloc[1]) # Output: Second row (index 1)

print(df.iloc[1])  # Output: Second row (index 1)

Accessing a specific cell: Use df.at[row_index, 'column_name']

print(df.at[1, 'Age']) # Output: 30 (second row, Age column)

print(df.at[1, 'Age'])  # Output: 30 (second row, Age column)

6. Modifying Data

To modify or update data in a DataFrame, you can use the following methods:

Adding a new column: Assign a value to a new column name.

df['Country'] = ['USA', 'USA', 'USA']

print(df)

df['Country'] = ['USA', 'USA', 'USA'] print(df)

df['Country'] = ['USA', 'USA', 'USA']
print(df)

Updating an existing column: Assign a new value to an existing column.

df['Age'] = df['Age'] + 1

print(df)

df['Age'] = df['Age'] + 1 print(df)

df['Age'] = df['Age'] + 1
print(df)

7. Filtering Data

Filtering rows based on conditions is a common operation in data manipulation:

Filter by condition: You can filter rows based on a condition applied to a column.

adults = df[df['Age'] > 30]

print(adults)

adults = df[df['Age'] > 30] print(adults)

adults = df[df['Age'] > 30]
print(adults)

Multiple conditions: Use & (and) or | (or) to apply multiple conditions.

result = df[(df['Age'] > 30) & (df['City'] == 'Chicago')]

print(result)

result = df[(df['Age'] > 30) & (df['City'] == 'Chicago')] print(result)

result = df[(df['Age'] > 30) & (df['City'] == 'Chicago')]
print(result)

8. Data Cleaning

Cleaning data is crucial in any data manipulation process. Pandas provides several functions to handle missing data:

Checking for missing data: Use df.isnull() to identify missing values.

print(df.isnull()) # Output: Boolean DataFrame indicating missing values

print(df.isnull())  # Output: Boolean DataFrame indicating missing values

Filling missing data: You can fill missing values with a specified value.

df['Age'].fillna(30, inplace=True) # Fill missing age with 30

df['Age'].fillna(30, inplace=True)  # Fill missing age with 30

Dropping missing data: Use df.dropna() to remove rows with missing values.

df.dropna(inplace=True) # Drop rows with missing values

df.dropna(inplace=True)  # Drop rows with missing values

9. Sorting Data

Sorting data by column values is a common operation:

Sort by a single column: Use df.sort_values(by='column_name')

df_sorted = df.sort_values(by='Age')

print(df_sorted)

df_sorted = df.sort_values(by='Age') print(df_sorted)

df_sorted = df.sort_values(by='Age')
print(df_sorted)

Sort by multiple columns: You can sort by multiple columns by passing a list.

df_sorted = df.sort_values(by=['City', 'Age'])

print(df_sorted)

df_sorted = df.sort_values(by=['City', 'Age']) print(df_sorted)

df_sorted = df.sort_values(by=['City', 'Age'])
print(df_sorted)

10. Grouping Data

Grouping is useful for aggregating data based on certain columns:

Group by a column: Use df.groupby('column_name') to group data by a specific column.

grouped = df.groupby('City').mean() # Group by 'City' and calculate the mean for numerical columns

print(grouped)

grouped = df.groupby('City').mean() # Group by 'City' and calculate the mean for numerical columns print(grouped)

grouped = df.groupby('City').mean()  # Group by 'City' and calculate the mean for numerical columns
print(grouped)

Conclusion

Pandas is an essential library for Data Scientists when it comes to data manipulation. With its powerful tools for data cleaning, filtering, grouping, and transforming, you can efficiently process large datasets and prepare them for analysis or machine learning.