绘制多索引DataFrame条形图，其中颜色由类别确定

时间：2015-08-11 18:53:03

标签： python pandas matplotlib plot

我有一个多索引DataFrame，看起来像下面的数据。当我绘制数据时，图形如下所示。

如何绘制条形图，条形图的颜色由我所需的类别确定（例如：＆＃39; City＆＃39;）。因此，无论年份如何，属于同一城市的所有酒吧都具有相同的颜色。例如：在下图中，所有ATL条应为红色，而所有MIA条应为蓝色。

enter image description here 

City            ATL                                    MIA               \
Year           2010         2011         2012         2010         2011   
Taste                                                                     
Bitter  3159.861983  3149.806667  2042.348937  3124.586470  3119.541240   
Sour    1078.897032  3204.689424  3065.818991  2084.322056  2108.568495   
Spicy   5280.847114  3134.597728  1015.311288  2036.494136  1001.532560   
Sweet   1056.169267  1015.368646  4217.145165  3134.734027  4144.826118   

City                 
Year           2012  
Taste                
Bitter  1070.925695  
Sour    3178.131540  
Spicy   3164.382635  
Sweet   3173.919338

以下是我的代码：

import sys
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import random

matplotlib.style.use('ggplot')

def main():

    taste = ['Sweet','Spicy','Sour','Bitter']
    store = ['Asian','Italian','American','Greek','Mexican']

    df1 = pd.DataFrame({'Taste':[random.choice(taste) for x in range(10)],
                       'Store':[random.choice(store) for x in range(10)],
                       'Sold':1000+100*np.random.rand(10)})

    df2 = pd.DataFrame({'Taste':[random.choice(taste) for x in range(10)],
                       'Store':[random.choice(store) for x in range(10)],
                       'Sold':1000+100*np.random.rand(10)})

    df3 = pd.DataFrame({'Taste':[random.choice(taste) for x in range(10)],
                       'Store':[random.choice(store) for x in range(10)],
                       'Sold':1000+100*np.random.rand(10)})

    df4 = pd.DataFrame({'Taste':[random.choice(taste) for x in range(10)],
                       'Store':[random.choice(store) for x in range(10)],
                       'Sold':1000+100*np.random.rand(10)})

    df5 = pd.DataFrame({'Taste':[random.choice(taste) for x in range(10)],
                       'Store':[random.choice(store) for x in range(10)],
                       'Sold':1000+100*np.random.rand(10)})


    df6 = pd.DataFrame({'Taste':[random.choice(taste) for x in range(10)],
                       'Store':[random.choice(store) for x in range(10)],
                       'Sold':1000+100*np.random.rand(10)})



    df1['Year'] = '2010'
    df1['City'] = 'MIA'

    df2['Year'] = '2011'
    df2['City'] = 'MIA'

    df3['Year'] = '2012'
    df3['City'] = 'MIA'

    df4['Year'] = '2010'
    df4['City'] = 'ATL'

    df5['Year'] = '2011'
    df5['City'] = 'ATL'

    df6['Year'] = '2012'
    df6['City'] = 'ATL'


    DF = pd.concat([df1,df2,df3,df4,df5,df6])
    DFG = DF.groupby(['Taste', 'Year', 'City'])
    DFGSum = DFG.sum().unstack(['Year','City']).sum(axis=1,level=['City','Year'])
    print DFGSum

    '''
    In my plot, I want the color of the bars to be determined by the "City".
    For example: All "ATL" bar colors will be the same regardless of the year.
    '''
    DFGSum.plot(kind='bar')


    plt.show()

if __name__ == '__main__':
    main()

2 个答案:

答案 0 :(得分：3)

编辑包括颜色循环和任意数量的城市

你需要指定一些额外的args才能让它看起来不错，但是这样的东西可能会起作用

if (rdr[3].ToString() != "" || rdr[3] != null) {
int something = Convert.ToInt32(rdr[3]);
}

enter image description here

虽然在这里你不知道哪个酒吧对应哪一年......

替代解决方案

您还可以制作一种略有不同的图表，该图表会在刻度标签中保留年份信息。这可以推广到任意数量的城市，并保持默认的颜色样式

import itertools # for color cycling

# specify the colors you want for each city
color_cycle = itertools.cycle( plt.rcParams['axes.color_cycle']  )
colors = { cty:color_cycle.next() for cty in DF.City.unique() }

#spcify the relative position of each bar
n = len(list(DFGSum))
positions = linspace(-n/2., n/2., n)

# plot each column individually
for i,col in enumerate(list(DFGSum)):
    c = colors[col[0]]
    pos = positions[i]
    DFGSum[col].plot(kind='bar', color=c, 
                     position=pos, width=0.05)

plt.legend()
plt.show()

enter image description here

答案 1 :(得分：3)

我找到了解决自己问题的方法。我对最初回答我问题的@ dermen给予了部分赞誉。我的回答是受他的方法的启发。

虽然@ dermen的解决方案是正确的，但我觉得我需要一种方法，我不必手动调整条的宽度或担心位置。

以下解决方案可以适应任意数量的城市，以及属于该城市的年度数据。重要的是要知道在下面的解决方案中，绘制的DataFrame是一个多级DataFrame。解决方案可能会在DataFrame排序的情况下中断，因为绘图以特定顺序发生。

enter image description here 

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

matplotlib.style.use('ggplot')


taste = ['Sweet','Spicy','Sour','Bitter']
store = ['Asian','Italian','American','Greek','Mexican']

df1 = pd.DataFrame({'Taste':[random.choice(taste) for x in range(10)],
                   'Store':[random.choice(store) for x in range(10)],
                   'Sold':1000+100*np.random.rand(10)})

df2 = pd.DataFrame({'Taste':[random.choice(taste) for x in range(10)],
                   'Store':[random.choice(store) for x in range(10)],
                   'Sold':1000+100*np.random.rand(10)})

df3 = pd.DataFrame({'Taste':[random.choice(taste) for x in range(10)],
                   'Store':[random.choice(store) for x in range(10)],
                   'Sold':1000+100*np.random.rand(10)})

df4 = pd.DataFrame({'Taste':[random.choice(taste) for x in range(10)],
                   'Store':[random.choice(store) for x in range(10)],
                   'Sold':1000+100*np.random.rand(10)})

df5 = pd.DataFrame({'Taste':[random.choice(taste) for x in range(10)],
                   'Store':[random.choice(store) for x in range(10)],
                   'Sold':1000+100*np.random.rand(10)})


df6 = pd.DataFrame({'Taste':[random.choice(taste) for x in range(10)],
                   'Store':[random.choice(store) for x in range(10)],
                   'Sold':1000+100*np.random.rand(10)})


df7 = pd.DataFrame({'Taste':[random.choice(taste) for x in range(10)],
                   'Store':[random.choice(store) for x in range(10)],
                   'Sold':1000+100*np.random.rand(10)})


df8 = pd.DataFrame({'Taste':[random.choice(taste) for x in range(10)],
                   'Store':[random.choice(store) for x in range(10)],
                   'Sold':1000+100*np.random.rand(10)})

df9 = pd.DataFrame({'Taste':[random.choice(taste) for x in range(10)],
                   'Store':[random.choice(store) for x in range(10)],
                   'Sold':1000+100*np.random.rand(10)})


df10 = pd.DataFrame({'Taste':[random.choice(taste) for x in range(10)],
                   'Store':[random.choice(store) for x in range(10)],
                   'Sold':1000+100*np.random.rand(10)})



df1['Year'] = '2010'
df1['City'] = 'MIA'

df2['Year'] = '2011'
df2['City'] = 'MIA'

df3['Year'] = '2012'
df3['City'] = 'MIA'

df4['Year'] = '2010'
df4['City'] = 'ATL'

df5['Year'] = '2011'
df5['City'] = 'ATL'

df6['Year'] = '2012'
df6['City'] = 'ATL'


df7['Year'] = '2013'
df7['City'] = 'ATL'

df8['Year'] = '2014'
df8['City'] = 'ATL'

df9['Year'] = '2013'
df9['City'] = 'CHI'

df10['Year'] = '2014'
df10['City'] = 'CHI'

DF = pd.concat([df1,df2,df3,df4,df5,df6,df7,df8,df9,df10])

DFG = DF.groupby(['Taste', 'Year', 'City'])
DFGSum = DFG.sum().unstack(['Year','City']).sum(axis=1,level=['City','Year'])
#DFGSum is a multilevel DataFrame 

import itertools 
color_cycle = itertools.cycle( plt.rcParams['axes.color_cycle']  )

plot_colors = [] #Array for a squenece of colors to be plotted 

for city in DFGSum.columns.get_level_values('City').unique(): 
  set_color = color_cycle.next() #Set the color for the city 
  for year in DFGSum[city].columns.get_level_values('Year').unique():
    plot_colors.append(set_color)
    #For each unqiue city, all the yearly data belonging to that city will have the same color 

DFGSum.plot(kind='bar',color=plot_colors)
# The color pramater of the plot function allows a list of colors sequences to be specified
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