英文:
How to prevent bar plots from superimposing on each other in pandas?
问题
我正在使用pandas处理一些数据，并想基于这些数据生成两个单独的条形图。然而，我的两个条形图却重叠在一起（它们在同一图表中）。以下是我的代码：
import mathimport pandas as pdpd.options.mode.chained_assignment = None # default='warn'import numpy as npimport matplotlib.pyplot as pltfrom openpyxl import load_workbookdef gen(fileIn):DF= pd.read_excel(fileIn)overall = DF['Awesome'].value_counts(normalize=True) # 获取“Awesome”列中每个唯一值的相对频率print(overall.plot(kind='bar', figsize=(10,5)))spec = DF['Not Awesome'].value_counts(normalize=True)print(spec.plot(kind='bar', color='red', figsize=(10,5)))gen("文件路径")
这是输出的结果：
正如您所看到的，红色来自“Not Awesome”列的值重叠在“Awesome”列的相对频率值上。我只希望这两个条形图是分开的。我查看了https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html 上的绘图函数的文档，但似乎没有可以用来关闭默认叠加的参数。
英文:
I am working with some data in pandas and I want to generate 2 separate bar plots based on the data. However, my two bar plots instead get superimposed on each-other (they are in the same graph). Here is my code:
import mathimport pandas as pdpd.options.mode.chained_assignment = None # default='warn'import numpy as npimport matplotlib.pyplot as pltfrom openpyxl import load_workbookdef gen(fileIn):DF= pd.read_excel(fileIn)overall = DF['Awesome'].value_counts(normalize = True) # Get the relative frequency of each unique value in the column Awesomeprint(overall.plot(kind = 'bar', figsize = (10,5)))spec = DF['Not Awesome'].value_counts(normalize = True)print(spec.plot(kind = 'bar', color = 'red', figsize = (10,5)))gen("my file path")
This is the output:
As you can see, the red color from the 'Not Awesome' column get supplanted on the 'Awesome' column's relative frequency values. I just want the two bar plots to be separate. I looked at the documentation in https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html for the plot function, however, there don't seem to be any parameters I can use to turn off the superimposition which seems to be a default.
答案1
得分: 1
你需要创建两个子图：
def gen(fileIn):DF = pd.read_excel(fileIn)fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))overall = DF['Awesome'].value_counts(normalize=True)print(overall.plot(kind='bar', ax=ax1))spec = DF['Not Awesome'].value_counts(normalize=True)print(spec.plot(kind='bar', color='red', ax=ax2))
输出：
但你也可以想要：
def gen(fileIn):DF = pd.read_excel(fileIn)overall = DF['Awesome'].value_counts(normalize=True)spec = DF['Not Awesome'].value_counts(normalize=True)ax = (pd.concat([overall, spec], keys=['Awesome', 'Not Awesome'], axis=1).plot(kind='bar', color=['blue', 'red'], figsize=(10,5)))
输出：
英文:
You have to create two subplots:
def gen(fileIn):DF = pd.read_excel(fileIn)fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))overall = DF['Awesome'].value_counts(normalize=True)print(overall.plot(kind='bar', ax=ax1))spec = DF['Not Awesome'].value_counts(normalize=True)print(spec.plot(kind='bar', color='red', ax=ax2))
Output:
But you can also want:
def gen(fileIn):DF = pd.read_excel(fileIn)overall = DF['Awesome'].value_counts(normalize=True)spec = DF['Not Awesome'].value_counts(normalize=True)ax = (pd.concat([overall, spec], keys=['Awesome', 'Not Awesome'], axis=1).plot(kind='bar', color=['blue', 'red'], figsize=(10,5)))
Output:
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