python pyplot-格式绘制图形的轴轴为2％至小数点位置

发布于 2025-02-04 11:57:16 字数 964 浏览 2 评论 0原文

我试图将CDC的护理数据延迟作为线路图，但是在格式化Y轴时遇到了一些麻烦，因此它是百分之一的百分比。我还希望X轴每年在选定的范围内显示。这是我的代码：

import pandas as pd
from isolation import isolate_total_stub, isolate_age_stub
import matplotlib.pyplot as plt


# very simple extraction, drop some columns and check some data
cdc_data = pd.read_csv('CDC_Delay_of_Care_Data.csv')

# separate the categories of delayed care
delay_of_medical_care = cdc_data[cdc_data.PANEL == 'Delay or nonreceipt of needed medical care due to cost']

# isolate the totals stub
total_delay_of_medical_care = isolate_total_stub(delay_of_medical_care)

x_axis = total_delay_of_medical_care.YEAR
y_axis = total_delay_of_medical_care.ESTIMATE
plt.plot(x_axis, y_axis)

plt.xlabel('Year')
plt.ylabel('Percentage')

plt.show()

显示的图形如下： line Graph

对不起，我是新手，我现在搜索了一个小时，而不是一个小时继续搜索答案，我认为询问StackoverFlow会更有成效。

谢谢你的宝贵时间。

原文

I am trying to represent CDC Delay of Care data as a line graph but am having some trouble formatting the y axis so that it is a percentage to the hundredths place. I would also like for the x axis to show every year in the range selected.
Here is my code:

import pandas as pd
from isolation import isolate_total_stub, isolate_age_stub
import matplotlib.pyplot as plt


# very simple extraction, drop some columns and check some data
cdc_data = pd.read_csv('CDC_Delay_of_Care_Data.csv')

# separate the categories of delayed care
delay_of_medical_care = cdc_data[cdc_data.PANEL == 'Delay or nonreceipt of needed medical care due to cost']

# isolate the totals stub
total_delay_of_medical_care = isolate_total_stub(delay_of_medical_care)

x_axis = total_delay_of_medical_care.YEAR
y_axis = total_delay_of_medical_care.ESTIMATE
plt.plot(x_axis, y_axis)

plt.xlabel('Year')
plt.ylabel('Percentage')

plt.show()

The graph that displays looks like this:
line graph

Excuse me for being a novice, I have been googling for an hour now and instead of continue to search for an answer I thought it would be more productive to ask StackOverflow.

Thank you for your time.

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七色彩虹 2025-02-11 11:57:16

要更改Y轴的格式，您可以使用set_major_formatter
以年轻的格式更改X轴，您需要使用set_major_locator，假设您的日期是在dateTime格式中
以更改X轴的格式，您可以再次使用set_major_formatter

我在下面显示带有虚拟数据的小示例。希望这有效。

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter
import matplotlib.dates as mdate

estimate = [8, 7.1, 11, 10.6, 8, 8.3]
year = ['2000-01-01', '2004-01-01', '2008-01-01', '2012-01-01', '2016-01-01', '2020-01-01']
year=pd.to_datetime(year) ## Convert string to datetime

plt.figure(figsize=(12,5)) ## Added so the Years don't overlap on each other
plt.plot(year, estimate)
plt.xlabel('Year')
plt.ylabel('Percentage')
plt.gca().yaxis.set_major_formatter(FormatStrFormatter('%.2f')) ## Makes X-axis label with two decimal points

locator = mdate.YearLocator()
plt.gca().xaxis.set_major_locator(locator) ## Changes datetime to years - 1 label per year
plt.gca().xaxis.set_major_formatter(mdate.DateFormatter('%Y')) ## Shows X-axis in Years
plt.gcf().autofmt_xdate() ## Rotates X-labels, if you want to use it 
plt.show()

输出图

To change the format of Y-axis, you can use set_major_formatter
To change X-axis to date in year format, you will need to use set_major_locator, assuming that your date is in datetime format
To change format of X-axis, you can again use the set_major_formatter

I am showing a small example below with dummy data. Hope this works.

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter
import matplotlib.dates as mdate

estimate = [8, 7.1, 11, 10.6, 8, 8.3]
year = ['2000-01-01', '2004-01-01', '2008-01-01', '2012-01-01', '2016-01-01', '2020-01-01']
year=pd.to_datetime(year) ## Convert string to datetime

plt.figure(figsize=(12,5)) ## Added so the Years don't overlap on each other
plt.plot(year, estimate)
plt.xlabel('Year')
plt.ylabel('Percentage')
plt.gca().yaxis.set_major_formatter(FormatStrFormatter('%.2f')) ## Makes X-axis label with two decimal points

locator = mdate.YearLocator()
plt.gca().xaxis.set_major_locator(locator) ## Changes datetime to years - 1 label per year
plt.gca().xaxis.set_major_formatter(mdate.DateFormatter('%Y')) ## Shows X-axis in Years
plt.gcf().autofmt_xdate() ## Rotates X-labels, if you want to use it 
plt.show()

Output plot