pandas 如何在python中制作帕累托图？
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How to make Pareto Chart in python?
提问by ImportanceOfBeingErnest
Pareto is very popular diagarm in Excel and Tableu. In excel we can easily draw a Pareto diagram but I found no easy way to draw the diagram in Python.
Pareto 是 Excel 和 Tableu 中非常流行的图表。在 excel 中，我们可以轻松地绘制帕累托图，但我发现在 Python 中没有简单的方法来绘制该图。
I have a pandas dataframe like this:
我有一个像这样的Pandas数据框：
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.DataFrame({'country': [177.0, 7.0, 4.0, 2.0, 2.0, 1.0, 1.0, 1.0]})
df.index = ['USA', 'Canada', 'Russia', 'UK', 'Belgium', 'Mexico', 'Germany', 'Denmark']
print(df)
country
USA 177.0
Canada 7.0
Russia 4.0
UK 2.0
Belgium 2.0
Mexico 1.0
Germany 1.0
Denmark 1.0
How to draw the Pareto diagram ? Using maybe pandas, seaborn, matplotlib etc?
帕累托图怎么画？可能使用Pandas、seaborn、matplotlib 等？
So far I have been able to make descending order bar chart. But its still remaining to put cumulative sum line plot on top of them.
到目前为止，我已经能够制作降序条形图。但是仍然需要将累积总和线图放在它们之上。
My attempt:
df.sort_values(by='country',ascending=False).plot.bar()
我的尝试：
df.sort_values(by='country',ascending=False).plot.bar()
Required plot:
所需情节：
回答by ImportanceOfBeingErnest
You would probably want to create a new column with the percentage in it and plot one column as bar chart and the other as a line chart in a twin axes.
您可能希望创建一个包含百分比的新列，并将其中一列绘制为条形图，将另一列绘制为双轴中的折线图。
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import PercentFormatter
df = pd.DataFrame({'country': [177.0, 7.0, 4.0, 2.0, 2.0, 1.0, 1.0, 1.0]})
df.index = ['USA', 'Canada', 'Russia', 'UK', 'Belgium', 'Mexico', 'Germany', 'Denmark']
df = df.sort_values(by='country',ascending=False)
df["cumpercentage"] = df["country"].cumsum()/df["country"].sum()*100
fig, ax = plt.subplots()
ax.bar(df.index, df["country"], color="C0")
ax2 = ax.twinx()
ax2.plot(df.index, df["cumpercentage"], color="C1", marker="D", ms=7)
ax2.yaxis.set_major_formatter(PercentFormatter())
ax.tick_params(axis="y", colors="C0")
ax2.tick_params(axis="y", colors="C1")
plt.show()
回答by crinix
More generalized version of ImportanceOfBeingErnest's code:
ImportanceOfBeingErnest 代码的更通用版本：
def create_pareto_chart(df, by_variable, quant_variable):
df.index = by_variable
df["cumpercentage"] = quant_variable.cumsum()/quant_variable.sum()*100
fig, ax = plt.subplots()
ax.bar(df.index, quant_variable, color="C0")
ax2 = ax.twinx()
ax2.plot(df.index, df["cumpercentage"], color="C1", marker="D", ms=7)
ax2.yaxis.set_major_formatter(PercentFormatter())
ax.tick_params(axis="y", colors="C0")
ax2.tick_params(axis="y", colors="C1")
plt.show()
And this one includes Pareto by grouping according to a threshold, too. For example: If you set it to 70, it will group minorities beyond 70 into one group called "Other".
这个也包括根据阈值分组的帕累托。例如：如果您将其设置为 70，它会将 70 岁以上的少数群体归为一组，称为“其他”。
def create_pareto_chart(by_variable, quant_variable, threshold):
total=quant_variable.sum()
df = pd.DataFrame({'by_var':by_variable, 'quant_var':quant_variable})
df["cumpercentage"] = quant_variable.cumsum()/quant_variable.sum()*100
df = df.sort_values(by='quant_var',ascending=False)
df_above_threshold = df[df['cumpercentage'] < threshold]
df=df_above_threshold
df_below_threshold = df[df['cumpercentage'] >= threshold]
sum = total - df['quant_var'].sum()
restbarcumsum = 100 - df_above_threshold['cumpercentage'].max()
rest = pd.Series(['OTHERS', sum, restbarcumsum],index=['by_var','quant_var', 'cumpercentage'])
df = df.append(rest,ignore_index=True)
df.index = df['by_var']
df = df.sort_values(by='cumpercentage',ascending=True)
fig, ax = plt.subplots()
ax.bar(df.index, df["quant_var"], color="C0")
ax2 = ax.twinx()
ax2.plot(df.index, df["cumpercentage"], color="C1", marker="D", ms=7)
ax2.yaxis.set_major_formatter(PercentFormatter())
ax.tick_params(axis="x", colors="C0", labelrotation=70)
ax.tick_params(axis="y", colors="C0")
ax2.tick_params(axis="y", colors="C1")
plt.show()
回答by Lucas Aimaretto
Another way is using the secondary_yparameter without using twinx():
另一种方法是使用secondary_y参数而不使用twinx()：
df['pareto'] = 100 *df.country.cumsum() / df.country.sum()
fig, axes = plt.subplots()
ax1 = df.plot(use_index=True, y='country', kind='bar', ax=axes)
ax2 = df.plot(use_index=True, y='pareto', marker='D', color="C1", kind='line', ax=axes, secondary_y=True)
ax2.set_ylim([0,110])
The parameter use_index=Trueis needed because your indexis your xaxis in this case. Otherwise you could've used x='x_Variable'.
该参数use_index=True是必需的，因为在这种情况下您index是您的x轴。否则你可以使用x='x_Variable'.
回答by venergiac
pareto chart for pandas.dataframe
pandas.dataframe 的帕累托图
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import PercentFormatter
def _plot_pareto_by(df_, group_by, column):
df = df_.groupby(group_by)[column].sum().reset_index()
df = df.sort_values(by=column,ascending=False)
df["cumpercentage"] = df[column].cumsum()/df[column].sum()*100
fig, ax = plt.subplots(figsize=(20,5))
ax.bar(df[group_by], df[column], color="C0")
ax2 = ax.twinx()
ax2.plot(df[group_by], df["cumpercentage"], color="C1", marker="D", ms=7)
ax2.yaxis.set_major_formatter(PercentFormatter())
ax.tick_params(axis="y", colors="C0")
ax2.tick_params(axis="y", colors="C1")
for tick in ax.get_xticklabels():
tick.set_rotation(45)
plt.show()
