绘制多索引DataFrame条形图,其中颜色由类别确定 [英] Plotting multiindex DataFrame bar plot where color is determined by category
问题描述
我有一个多索引DataFrame,看起来像下面的数据.当我绘制数据时,图形如下图所示.
I have a multiindex DataFrame that looks like the data below. When I plot the data, the graph looks like below.
如何绘制条形图,条形的颜色由所需的类别(例如:城市")决定.因此,与年份无关,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.
How can I plot a bar graph, where the color of the bars is determined by my desired category (ex: 'City'). Thus, all bars belonging to the same city have the same color, regardless of the year. For example: In the graph below, all ATL bars should be red, while all MIA bars should be blue.
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
下面是我的代码:
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()
推荐答案
我已经找到了解决自己问题的方法.我将部分归功于最初回答我的问题的@ dermen .我的回答是受他的方法启发的.
I have found a solution to my own question. I give partial credit to @dermen who originally answered my question. My answer was inspired by his approach.
尽管@dermen的解决方案是正确的,但我觉得我需要一种无需手动调整条形宽度或不必担心位置的方法.
Although @dermen's solution is correct, I felt I needed a method where I don't have to manually adjust the width of the bars or worry about positions.
以下解决方案可以适应任意数量的城市,以及该城市的年度数据.重要的是要知道,在下面的解决方案中,要绘制的DataFrame是多级DataFrame.该解决方案在对DataFrame进行排序的情况下可能会中断,因为绘制是按照特定的顺序进行的.
The solution below can be adapted to arbitrary amount of cities, and the yearly data belonging to that city. It is important to know that in the solution below, the DataFrame being plotted is a multilevel DataFrame. The solution may break in situations where the DataFrame is sorted, because plotting occurs in a specific order.
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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