[英]Create grouped/stacked bar plots from multiple categories containing several labels inside a pandas dataframe
I have the following pandas
dataframe ( df
) [ only an excerpt of the full dataframe ]: 我有以下
pandas
数据框( df
)[ 仅是完整数据框的一部分 ]:
Name Cat_1 Cat_2
0 foo P Apples, Pears, Cats
1 bar R, M Apples
2 bla E Pears
3 blu F Cats, Pears
4 boo G Apples, Pears
5 faa P, E Apples, Cats
I would like to create bar plots that are build from Cat_1
and Cat_2
. 我想创建从
Cat_1
和Cat_2
构建的Cat_2
。 These columns contain multiple tags, which have to be use for plotting. 这些列包含多个标记,这些标记必须用于绘图。
Currently, I am running this simple code to plot Cat_1
: 当前,我正在运行以下简单代码来绘制
Cat_1
:
import pandas as pd
from matplotlib import pyplot as plt
fig, ax = plt.subplots(figsize = (4,4))
s = df["Cat_1"].str.split(", ", expand = True).stack()
s.value_counts().plot(kind = 'bar', ax = ax)
This returns a nice bar plot for each of the different labels in Cat_1
allowing multiple assignments (as intended). 这会为
Cat_1
每个不同标签返回一个漂亮的条形图,允许进行多个分配(按预期进行)。
One could apply the same to Cat_2
and obtain a separate plot with the respective labels. 可以将相同的内容应用于
Cat_2
并获得带有相应标签的单独图。
However, I want to have a single plot that is first "stacked" by Cat_1
and subsequently the values are counted for Cat_2
. 但是,我希望有一个图,该图首先由
Cat_1
“堆叠”,然后为Cat_2
计算值。
I guess a way to think of this is to build a nested dictionary that would look like the following: 我想想办法是建立一个嵌套的字典,如下所示:
{"P": {"Apples": 2, "Pears": 1, "Cats": 2}, "R": {"Apples": 1}, ....}
but at the same time keep track of the total count of Cat_1
. 但同时要跟踪
Cat_1
的总数。
It does not matter whether its a grouped or stacked bar chart in the end. 到底是分组条形图还是堆叠条形图都没关系。
Please take a look a the enclosed figure for a more visual idea: 请看一下随附的图,以获得更直观的想法:
This should get you pretty close if I understand correctly. 如果我理解正确的话,这应该可以让您接近。
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
df = pd.DataFrame(columns=['Name', 'Cat_1', 'Cat_2'])
df['Name'] = ['foo', 'bar', 'bla', 'blu', 'boo', 'faa']
df['Cat_1'] = ['P', 'R, M', 'E', 'F', 'G', 'P, E']
df['Cat_2'] = ['Apples, Pears, Cats', 'Apples', 'Pears', 'Cats, Pears', 'Apples, Pears', 'Apples, Cats']
# arrange data simply prepopulate with zero
df_pl = pd.DataFrame(columns=df["Cat_1"].str.split(", ", expand=True).stack().unique().tolist(),
index=df["Cat_2"].str.split(", ", expand=True).stack().unique().tolist(),
data=0)
# get chunk size for each combination
for x in df_pl.columns:
ind = df.Cat_1.str.contains(x)
for name in df_pl.index:
df_pl.set_value(name, x, df.loc[ind, 'Cat_2'].str.contains(name).sum())
N = len(df_pl.columns)
ind = np.arange(N) # the x locations for the groups
width = 0.35 # the width of the bars: can also be len(x) sequence
plotted = []
p = {}
for name in df_pl.index:
bottoms = df_pl.index.isin(plotted).sum()
p[name] = plt.bar(ind, df_pl.loc[name].values.tolist(), bottom=bottoms)
plotted.append(name)
plt.ylabel('y_label')
plt.title('some plot')
plt.xticks(ind, df_pl.columns.tolist())
plt.legend(p.values(), p.keys())
plt.show()
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.