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Master the Art of Data Wrangling with Pandas
Introduction
Pandas is an open-source library for data analysis and manipulation in Python. It is one of the most widely used libraries for data analysis and has been adopted by many organizations due to its simplicity and versatility.
In this article, we will explore 5 advanced functions in Pandas to help you improve your efficiency when working with Pandas library. These functions will help you manipulate, transform, and aggregate your data with ease.
1. pd.cut and pd.qcut: Binning continuous variables into categorical ones
pd.cut() and pd.qcut() are two functions in Pandas that allow you to bin continuous variables into categorical ones. This is useful when you want to group your data into a smaller number of bins and analyze the distribution of your data.
pd.cut() is used to bin data into a specific number of bins with equal widths. For example:
import pandas as pd
import numpy as np
data = np.random.randn(100)
bins = [-np.inf, -1, 0, 1, np.inf]
labels = ['very_low', 'low', 'medium', 'high']
df = pd.DataFrame({'data': data})
df['binned'] = pd.cut(df['data'], bins=bins, labels=labels)
