Pandas: Most Widely Used Functions and How to Use Them

In the previous articles, we introduced the Pandas library and its applications in different fields of society. In this article, we will dive into the most widely used functions in Pandas and provide practical examples of how to use them effectively for various data manipulation tasks.

1. Reading and Writing Data

read_csv()

The read_csv() function is used to read data from a CSV file and store it in a DataFrame. You can specify various parameters, such as the delimiter, encoding, and column names.

import pandas as pd

# Read data from a CSV file
df = pd.read_csv('data.csv')

# Display the DataFrame
print(df)

to_csv()

The to_csv() function is used to write data from a DataFrame to a CSV file. You can specify parameters such as the file path, delimiter, and encoding.

# Write data to a CSV file
df.to_csv('output.csv', index=False)

2. Data Exploration

head()

The head() function displays the first n rows of the DataFrame. It's useful for getting an overview of the data.

# Display the first 5 rows
print(df.head())

tail()

The tail() function displays the last n rows of the DataFrame. It's useful for checking the end of the data.

# Display the last 5 rows
print(df.tail())

describe()

The describe() function generates a summary of the DataFrame's statistical information, such as the mean, standard deviation, and percentiles. It's useful for getting a quick overview of the data's distribution.

# Display statistical summary
print(df.describe())

info()

The info() function displays information about the DataFrame's columns, data types, and memory usage. It's useful for understanding the structure and size of the data.

# Display DataFrame information
print(df.info())

3. Data Cleaning

drop()

The drop() function is used to remove specified rows or columns from the DataFrame. You can use the axis parameter to specify whether to drop rows or columns.

# Drop a column
df = df.drop('Column_to_drop', axis=1)

# Drop a row
df = df.drop(5, axis=0)

fillna()

The fillna() function is used to replace missing values (NaN) in the DataFrame with specified values or methods. You can fill missing values with a constant value or use methods like 'ffill' (forward fill) or 'bfill' (backward fill) to propagate non-null values.

# Replace missing values with a constant value
df = df.fillna(0)

# Replace missing values using forward fill
df = df.fillna(method='ffill')

drop_duplicates()

The drop_duplicates() function is used to remove duplicate rows from the DataFrame. You can specify a subset of columns to consider when identifying duplicates.

# Remove all duplicate rows
df = df.drop_duplicates()

# Remove duplicate rows based on specific columns
df = df.drop_duplicates(subset=['Column1', 'Column2'])

4. Data Manipulation

sort_values()

The sort_values() function is used to sort the DataFrame by one or more columns. You can specify the sorting order using the ascending parameter.

# Sort DataFrame by a single column
df = df.sort_values('Column1')

# Sort DataFrame by multiple columns
df = df.sort_values(['Column1', 'Column2'], ascending=[True, False])

groupby()

The groupby() function is used to group the DataFrame based on one or more columns. You can then apply aggregation functions to the grouped data.

# Group DataFrame by a single column and calculate the mean

grouped_df = df.groupby('Column1').mean()

# Group DataFrame by multiple columns and count the occurrences
grouped_df = df.groupby(['Column1', 'Column2']).count()

merge()

The merge() function is used to merge two DataFrames based on common columns or indices. You can specify the type of merge (inner, outer, left, or right) using the how parameter.

# Merge two DataFrames on a common column
merged_df = pd.merge(df1, df2, on='Column1')

# Merge two DataFrames using left join
merged_df = pd.merge(df1, df2, on='Column1', how='left')

Conclusion

In this article, we have covered the most widely used functions in the Pandas library for data manipulation and analysis. With a solid understanding of these functions, you'll be well-equipped to tackle various data-related tasks in Python. In the next article, we will explore practical examples and use cases of the Pandas library.

Introduction to Pandas Library and Its Applications in Different Fields
Pandas: Most Widely Used Functions and How to Use Them
Pandas Practical Examples and Use Cases

Programming 101