Python数据处理从零开始----第四章（可视化）①②堆积柱状图目录使用Matplotlib和Pandas轻松堆积图表

# -*- coding: utf-8 -*-
"""
Created on Sat Dec  1 03:03:23 2018

@author: czh
"""
%clear
%reset -f
# In[*]
import numpy as np
import matplotlib.pyplot as plt
# In[*]

N = 5
menMeans = (20, 35, 30, 35, 27)
womenMeans = (25, 32, 34, 20, 25)
menStd = (2, 3, 4, 1, 2)
womenStd = (3, 5, 2, 3, 3)
ind = np.arange(N)    # the x locations for the groups
width = 0.35       # the width of the bars: can also be len(x) sequence

p1 = plt.bar(ind, menMeans, width, yerr=menStd)
p2 = plt.bar(ind, womenMeans, width,
             bottom=menMeans, yerr=womenStd)

plt.ylabel('Scores')
plt.title('Scores by group and gender')
plt.xticks(ind, ('G1', 'G2', 'G3', 'G4', 'G5'))
plt.yticks(np.arange(0, 81, 10))
plt.legend((p1[0], p2[0]), ('Men', 'Women'))

plt.show()

# In[*]

%matplotlib inline

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




data = [[2000, 2000, 2000, 2001, 2001, 2001, 2002, 2002, 2002],
        ['Jan', 'Feb', 'Mar', 'Jan', 'Feb', 'Mar', 'Jan', 'Feb', 'Mar'],
        [1, 2, 3, 4, 5, 6, 7, 8, 9]]

rows =  list(zip(data[0], data[1], data[2]))
headers = ['Year', 'Month', 'Value']

df = pd.DataFrame(rows, columns=headers)

df


# In[*]


fig, ax = plt.subplots(figsize=(10,7))  

months = df['Month'].drop_duplicates()
margin_bottom = np.zeros(len(df['Year'].drop_duplicates()))
colors = ["#006D2C", "#31A354","#74C476"]

for num, month in enumerate(months):
    values = list(df[df['Month'] == month].loc[:, 'Value'])

    df[df['Month'] == month].plot.bar(x='Year',y='Value', ax=ax, stacked=True, 
                                    bottom = margin_bottom, color=colors[num], label=month)
    margin_bottom += values

plt.show()

pivot_df = df.pivot(index='Year', columns='Month', values='Value')
pivot_df
#Note: .loc[:,['Jan','Feb', 'Mar']] is used here to rearrange the layer ordering
pivot_df.loc[:,['Jan','Feb', 'Mar']].plot.bar(stacked=True, color=colors, figsize=(10,7))