Working with Pandas DataFrames in Python

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In this blog post, we will explore how to append rows to an existing pandas DataFrame. We’ll focus on a specific use case where the number of rows depends on a comprehension list.

Introduction to Pandas DataFrames

A pandas DataFrame is a two-dimensional table of data with rows and columns. It’s a powerful data structure in Python that provides data analysis capabilities. In this section, we’ll introduce some basic concepts related to DataFrames.

Creating an Empty DataFrame

To create an empty DataFrame, you can use the pd.DataFrame() constructor without any arguments:

import pandas as pd

# Create an empty DataFrame with default integer index
df = pd.DataFrame()

# Create a DataFrame with custom columns
columns = ['chr', 'Start', 'End', 'type']
df = pd.DataFrame(columns=columns)

Understanding the Problem

The problem statement asks us to append rows to an existing DataFrame, where the number of rows depends on a comprehension list. The initial DataFrame x is created with custom columns ['chr', 'Start', 'End', 'type']. We’ll also define a comprehension list RANGE containing several intervals.

Defining the Comprehension List

The comprehension list RANGE contains several intervals defined as:

RANGE = [(212, 222),(866, 888),(152, 158)]

These intervals represent the start and end points of each range. We’ll use these intervals to generate new rows for the DataFrame.

Defining the Function to Fill the DataFrame

We define a function fill_df() that takes three arguments: df, junction, chr, and type. The function generates new rows based on the provided arguments:

def fill_df(df, junction, chr, type ):
    # Generate Start and End columns
    df['Start'] = [x.lower for x in junction]
    df['End'] =  [x.upper for x in junction]
    
    # Add chr and type columns
    df['chr'] = chr
    df['type'] = type
    
    return df

This function is used to generate new rows based on the provided intervals.

Creating the Initial DataFrame

We create an empty DataFrame x with custom columns:

columns = ['chr', 'Start', 'End', 'type']
x = pd.DataFrame(columns=columns)

We also define the comprehension list RANGE and calculate the interval:

INTERVAL= P.Interval(*[P.closed(x, y) for x, y in RANGE])

Solving the Problem

The problem statement mentions trying different approaches to append rows to the DataFrame. One of the solutions involves using pandas Series.

Using Pandas Series

We can use the pd.Series() constructor to convert the comprehension list into a pandas Series:

df['Start.pos'] = pd.Series([x.lower for x in junction])

This creates a new column Start.pos containing the lower-case values from the comprehension list.

Alternative Solutions

Another approach involves using the pd.concat() function to concatenate DataFrames. However, this method has limitations when dealing with variable-length lists of rows.

Using pd.concat()

We can use the pd.concat() function to concatenate multiple DataFrames:

new_rows = []
for interval in RANGE:
    new_junction = [x.lower for x in interval]
    new_row = {'Start': new_junction, 'End': new_junction[:1], 'chr': 1, 'type': 'DUP'}
    new_rows.append(new_row)

df = pd.concat([df, pd.DataFrame(new_rows)])

This approach requires us to manually create a list of dictionaries representing the new rows.

Conclusion

In this blog post, we explored how to append rows to an existing pandas DataFrame. We defined a comprehension list containing several intervals and used a function fill_df() to generate new rows based on those intervals. We introduced alternative solutions involving pandas Series and pd.concat(). The recommended approach is using pandas Series to convert the comprehension list into a Series, which provides a concise and efficient solution.

Example Use Cases

Here are some example use cases for appending rows to an existing DataFrame:

• Biological Data Analysis: You can use this technique to analyze biological data, such as genomic intervals or gene expression levels.
• Network Analysis: This method can be used to generate new nodes or edges in a network based on predefined intervals.

Future Work

In future blog posts, we’ll explore more advanced topics related to pandas DataFrames, such as data merging, grouping, and filtering. We’ll also delve into deeper analysis of biological data using pandas and Python libraries like scikit-learn and BioPython.

Last modified on 2025-02-27