Python pandas: Manipulating CSV files

Pandas is a powerful data analysis tool for the Python programming language, used for manipulating numerical tables and time series data. The library aims to make data handling easy, especially in the form of dataframes. A dataframe is a 2-dimensional data structure with labeled rows and columns, capable of holding data of different types.

How to install pandas

The pandas library can be easily installed using pip, the package management system for Python. Run the following command:

pip install pandas

Reading CSV files using pandas

Basic CSV file reading

You can read CSV files using the read_csv function in pandas.

1import pandas as pd
2
3df = pd.read_csv('file.csv')
4print(df)

Reading CSV files with different delimiters

If you want to read a CSV file with a delimiter other than a comma, you can specify the delimiter as an argument to the read_csv function. For example, to read a file separated by tabs, do the following:

1df = pd.read_csv('file.tsv', delimiter='\t')

Main options when reading CSV and how to use them

The read_csv function has many options that allow you to customize how you read the file. For example, if you are reading a file without headers, specify header=None.

1df = pd.read_csv('file.csv', header=None)

Also, to set a particular column as the index, use the index_col parameter.

1df = pd.read_csv('file.csv', index_col=0)

Writing CSV files using pandas

Outputting a dataframe to CSV

You can save a pandas DataFrame object as a CSV file using the to_csv method.

1df.to_csv('output.csv')

Main options when writing CSV and how to use them

The to_csv function also has many options. For example, if you don’t want to include the index in the output file, specify index=False.

1df.to_csv('output.csv', index=False)

Also, if you want to output only certain columns, use the columns parameter.

1df.to_csv('output.csv', columns=['column1', 'column2'])

Basic processing and analysis of CSV data

Data filtering and sorting

Pandas provides many features for filtering and sorting data within a dataframe.

1# Filter rows that meet certain conditions
2filtered_df = df[df['column1'] > 50]
3
4# Sort
5sorted_df = df.sort_values('column1')

Data aggregation and statistics

Pandas provides methods for calculating statistical information about data and aggregating data.

1# Calculate average
2mean_value = df['column1'].mean()
3
4# Group and aggregate data
5grouped_df = df.groupby('column1').sum()

Data visualization

Pandas is integrated with the matplotlib library, allowing you to easily visualize your data.

1df.plot(kind='bar')

Differences and use cases compared to the standard csv library

Python provides a csv module as standard, but pandas provides much more powerful data analysis features. For advanced data manipulations such as data manipulation in dataframe format, handling missing values, support for multiple data types, and statistical functions, it is recommended to use pandas.

On the other hand, the csv module is lighter than pandas and can process each row without loading large amounts of data into memory, making it suitable for simple CSV operations or handling large files.