Python DataFrame-用分组列(至少两列)为数据框绘制条形图 [英] Python DataFrame - plot a bar chart for data frame with grouped-by columns (at least two columns)
问题描述
我一直在努力使用matlplotlib在python中重新创建此Excel图形:
数据在数据框中；我正在尝试自动化生成该图的过程.
我尝试拆开数据框，进行子图绘制，但是我没有设法创建"Zone"区域.索引在Excel中是如此优雅.我已经成功地在没有此区域"的情况下绘制了图表.索引，但这并不是我真正想做的.
这是我的代码:
data = pd.DataFrame({工厂区":["AMERICAS"，"APAC"，"APAC"，"APAC"，"APAC"，"APAC"，"EMEA"，"EMEA"，"EMEA"，"EMEA"]，'工厂名称':[巧克力工厂"，蜡笔工厂"，"Jobs Ur Us"，"Gibberish US"，"Lil Grey"，"Toys R Us"，"Food Inc."，宠物店"，邦邦工厂"，家乐福"]，生产第1天":[24,1,9,29,92,79,4,90,42,35]，生产第2天":[2,43,17,5,31,89,44,49,34,84]})df = pd.DataFrame(data)打印(df)#没有FactoryZone，它可以工作:df = df.drop(['工厂区域']，轴= 1)图片= df.plot(种类=栏") 数据如下:
未命名:0 FactoryZone工厂名称生产第1天生产第2天0 1美洲巧克力工厂24 431 2美国蜡笔厂1 172 3 EMEA宠物店9 53 4 EMEA糖果工厂29 314 5亚太地区Lil Gray 92 895 6美国乔布斯我们79 446 7亚太地区玩具反斗城4 497 8 EMEA家乐福90 348 9美国胡言乱语美国42 849 10亚太食品公司35 62 您可以通过首先创建
参考文献:Quang Hoang的答案， gyx-hh的答案
I've been struggling to recreate this Excel graph in python using matlplotlib:
The data is in a dataframe; I'm trying to automate the process of generating this graph.
I've tried unstacking my dataframe, subplotting, but I haven't managed to create the "Zone" index which is so elegant in Excel. I have successfully managed to plot the graph without this "Zone" index, but that's not really what I want to do.
Here is my code:
data = pd.DataFrame(
{
'Factory Zone':
["AMERICAS","APAC","APAC","APAC","APAC","APAC","EMEA","EMEA","EMEA","EMEA"],
'Factory Name':
["Chocolate Factory","Crayon Factory","Jobs Ur Us", "Gibberish US","Lil Grey", "Toys R Us","Food Inc.",
"Pet Shop", "Bonbon Factory","Carrefour"],
'Production Day 1':
[24,1,9,29,92,79,4,90,42,35],
'Production Day 2':
[2,43,17,5,31,89,44,49,34,84]
})
df = pd.DataFrame(data)
print(df)
# Without FactoryZone, it works:
df = df.drop(['Factory Zone'], axis=1)
image = df.plot(kind="bar")
And the data looks like this:
Unnamed: 0 FactoryZone Factory Name Production Day 1 Production Day 2
0 1 AMERICAS Chocolate Factory 24 43
1 2 AMERICAS Crayon Factory 1 17
2 3 EMEA Pet Shop 9 5
3 4 EMEA Bonbon Factory 29 31
4 5 APAC Lil Grey 92 89
5 6 AMERICAS Jobs Ur Us 79 44
6 7 APAC Toys R Us 4 49
7 8 EMEA Carrefour 90 34
8 9 AMERICAS Gibberish US 42 84
9 10 APAC Food Inc. 35 62
You can create this plot by first creating a MultiIndex for your hierarchical dataset where level 0 is the Factory Zone and level 1 is the Factory Name:
import numpy as np # v 1.19.2
import pandas as pd # v 1.1.3
import matplotlib.pyplot as plt # v 3.3.2
df = pd.DataFrame(
{'Factory Zone': ['AMERICAS', 'AMERICAS', 'AMERICAS', 'AMERICAS', 'APAC',
'APAC', 'APAC', 'EMEA', 'EMEA', 'EMEA'],
'Factory Name': ['Chocolate Factory', 'Crayon Factory', 'Jobs Ur Us',
'Gibberish US', 'Lil Grey', 'Toys R Us', 'Food Inc.',
'Pet Shop', 'Bonbon Factory','Carrefour'],
'Production Day 1': [24,1,9,29,92,79,4,90,42,35],
'Production Day 2': [2,43,17,5,31,89,44,49,34,84]
})
df.set_index(['Factory Zone', 'Factory Name'], inplace=True)
df
# Production Day 1 Production Day 2
# Factory Zone Factory Name
# AMERICAS Chocolate Factory 24 2
# Crayon Factory 1 43
# Jobs Ur Us 9 17
# Gibberish US 29 5
# APAC Lil Grey 92 31
# Toys R Us 79 89
# Food Inc. 4 44
# EMEA Pet Shop 90 49
# Bonbon Factory 42 34
# Carrefour 35 84
Like Quang Hoang has proposed, you can create a subplot for each zone and stick them together. The width of each subplot must be corrected according to the number of factories by using the width_ratios argument in the gridspec_kw dictionary so that all the columns have the same width. Then there are limitless formatting choices to make.
In the following example, I choose to show separation lines only between zones by using the minor tick marks for this purpose. Also, because the figure width is limited here to 10 inches only, I rewrite the longer labels on two lines.
# Create figure with a subplot for each factory zone with a relative width
# proportionate to the number of factories
zones = df.index.levels[0]
nplots = zones.size
plots_width_ratios = [df.xs(zone).index.size for zone in zones]
fig, axes = plt.subplots(nrows=1, ncols=nplots, sharey=True, figsize=(10, 4),
gridspec_kw = dict(width_ratios=plots_width_ratios, wspace=0))
# Loop through array of axes to create grouped bar chart for each factory zone
alpha = 0.3 # used for grid lines, bottom spine and separation lines between zones
for zone, ax in zip(zones, axes):
# Create bar chart with grid lines and no spines except bottom one
df.xs(zone).plot.bar(ax=ax, legend=None, zorder=2)
ax.grid(axis='y', zorder=1, color='black', alpha=alpha)
for spine in ['top', 'left', 'right']:
ax.spines[spine].set_visible(False)
ax.spines['bottom'].set_alpha(alpha)
# Set and place x labels for factory zones
ax.set_xlabel(zone)
ax.xaxis.set_label_coords(x=0.5, y=-0.2)
# Format major tick labels for factory names: note that because this figure is
# only about 10 inches wide, I choose to rewrite the long names on two lines.
ticklabels = [name.replace(' ', '\n') if len(name) > 10 else name
for name in df.xs(zone).index]
ax.set_xticklabels(ticklabels, rotation=0, ha='center')
ax.tick_params(axis='both', length=0, pad=7)
# Set and format minor tick marks for separation lines between zones: note
# that except for the first subplot, only the right tick mark is drawn to avoid
# duplicate overlapping lines so that when an alpha different from 1 is chosen
# (like in this example) all the lines look the same
if ax.is_first_col():
ax.set_xticks([*ax.get_xlim()], minor=True)
else:
ax.set_xticks([ax.get_xlim()[1]], minor=True)
ax.tick_params(which='minor', length=55, width=0.8, color=[0, 0, 0, alpha])
# Add legend using the labels and handles from the last subplot
fig.legend(*ax.get_legend_handles_labels(), frameon=False, loc=(0.08, 0.77))
fig.suptitle('Production Quantity by Zone and Factory on both days', y=1.02, size=14);
References: the answer by Quang Hoang, this answer by gyx-hh
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