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binscatter
Dockerfile
README.md
requirements.txt
setup.py

README.md

binscatter is inspired by Stata's binscatter, described fully by Michael Stepner here. You can use it in essentially the same way you use Matplotlib functions like plot and scatter. A more extensive description of this package is here.

Getting started

  1. Copy and paste: Binscatter's meaningful code consists of consists of just one file. You can copy binscatter/binscatter.py into the directory the rest of your code is in.

  2. Install via pip: To make it easier to use binscatter in multiple projects and directories, open a terminal and

Usage

import binscatter
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt

# Create fake data
n_obs = 1000
data = pd.DataFrame({'experience': np.random.poisson(4, n_obs) + 1})
data['tenure'] = data['experience'] + np.random.normal(0, 1, n_obs)
data['wage'] = data['experience'] + data['tenure'] + np.random.normal(0, 1, n_obs)
fig, axes = plt.subplots(2)

# Binned scatter plot of wage vs tenure
axes[0].binscatter(data, 'wage', 'tenure')
axes[0].set_ylabel('Wage')
axes[0].set_ylabel('Tenure')

# Binned scatter plot that partials out the effect of experience
axes[1].binscatter(data, 'wage', 'tenure', controls=['experience'])
axes[1].set_xlabel('Tenure (residualized)')
axes[1].set_ylabel('Wage (residualized, recentered)')
plt.show()