Pandas #

• Documentation

Pandas is a popular data manipulation library for Python. It provides data structures and functions necessary to handle and analyze data efficiently and with ease. Pandas primarily uses two types of data structures: Series (1-dimensional) and DataFrame (2-dimensional), which can handle diverse types of data such as integers, floating-point numbers, strings, and date/time formats. The library offers a vast array of operations such as slicing, indexing, joining, merging, reshaping, handling missing data, and applying mathematical operations. It is also equipped with robust I/O tools that can read and write data from a wide variety of sources such as CSV, Excel, SQL databases, and even the clipboard. Pandas' integration with other Python libraries like Matplotlib and Seaborn for data visualization and NumPy for mathematical operations make it an essential tool for data analysis and manipulation. Whether you're working in data science, finance, social sciences, or engineering, understanding how to use Pandas can be a critical asset.

Getting Started #

Installation #

You can install Plotly using pip or conda by running the following command in your terminal:

pip install pandas

or

conda install pandas

Import #

The pandas module can be imported using the following command

import pandas as pd

Read an Excel File #

You can use the pd.read_excel() function to load an Excel file.

data = pd.read_excel('file.xlsx')

Additional modules #

The read_excel() function requires the openpyxl package for .xlsx files and xlrd package for .xls files. If you haven't installed it yet, you can use pip to install it:

• For .xlsx:

pip install openpyxl

• For .xls:

pip install xlrd

Visualisation functions #

Pandas, in conjunction with Matplotlib, provides a number of easy-to-use methods for visualizing your data.

• Examples

Line plots #

import pandas as pd
import matplotlib.pyplot as plt

# assuming data is a pandas DataFrame
data['column_name'].plot()
plt.show()

Scatter plots #

data['column_name'].value_counts().plot(kind='bar')
plt.show()

Histograms #

data['column_name'].plot(kind='hist')
plt.show()

Box plots #

data.plot(kind='box')
plt.show()

Scatter plots #

data.plot(kind='scatter', x='column_name1', y='column_name2')
plt.show()

Pie Chart #

data['column_name'].value_counts().plot(kind='pie')
plt.show()

These are just some basic plots. You can also make more complex plots like hexagonal bin plots, area plots, etc. Note also that you can customize these plots in many ways (like changing colors, labels, axes, etc.) by using additional arguments and/or by utilizing matplotlib functionality.