目錄

• 數字的分佈
• Python中的直方圖和箱形圖
• 各種情況的對比

在閱讀前，讀一下Python-深入淺出資料分析-總結會更好點，以後遇到問題比如程式碼執行不了，再讀讀也行，>-_-<

數字的分佈

首先書中數落的一下Excel的直方圖功能，其實呢，Excel2016已經改進了很多了

Python中的直方圖和箱形圖

用不了幾行程式碼，是不是比Excel簡單美觀點

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

%matplotlib inline

df = pd.read_csv('./hfda_ch09_employees.csv', skiprows=1, names =['staff_num', 'received', 'negotiated', 'gender', 'year'])

fig = plt.figure(figsize=(12, 6))

ax1 = fig.add_subplot(1, 3, 1)
l = ax1.boxplot(df['received'].values)

ax2 = fig.add_subplot(1, 2, 2)
l = ax2.hist(df['received'], bins=50)
 

各種情況的對比

分析不同維度的數字分佈

fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots(nrows=3, ncols=2, figsize=(16,16))

ax1.hist(df['received'][df['year']==2007], bins=50)
ax1.set_title('year=2007')
ax2.hist(df['received'][df['year']==2008], bins=50)
ax2.set_title('year=2008')

ax3.hist(df['received'][df['gender']=='M'], bins=50)
ax3.set_title('gender=M')
ax4.hist(df['received'][df['gender']=='F'], bins=50)
ax4.set_title('gender=F')

ax5.hist(df['received'][df['negotiated']], bins=50)
ax5.set_title('negotiated=TRUE')
ax6.hist(df['received'][~df['negotiated']], bins=50)
ax6.set_title('negotiated=FALSE')