经过多年的迭代以绘制不同的组值作为 pandas 中的条形图

问题描述

我有一个数据框，用于记录不同年份不同年份的观测次数.我正在尝试绘制一个小图，以显示不同年份不同年份的观测总数.对于每个位置，我希望以不同的颜色显示不同年份的总观测值.我的方法是先创建位置组，然后为每个位置组计算总观测值.(我不需要更改索引的日期，因为我要按位置分组).我无法使用以下代码来实现此目的.帮助将不胜感激.

I have a dataframe that records number of observations at different locations for different years. I am trying to make a barplot where I can show the total number of observations at different locations for different years. For each location, I want the total observations, for different years to be shown in different colors. My approach is to first make location groups and for each location group, calculate total observation. (I don't think I need to change the index to date - as I am grouping by location).I am not able to achieve this using the following code. Help will be much appreciated.

fig, ax = plt.subplots(figsize=(40,15))
date=df['date']
value=df['value']
df.date = pd.to_datetime(df.date)


year_start=2015
year_stop = 2019
#ax=plt.gca()

for year in range(year_start, year_stop+1):
    ax=plt.gca()
    m=df.groupby(['location']).agg({'value': ['count']})


    plt.ylim(0,45000)
    m.plot(kind='bar', legend = False, figsize=(30,15), fontsize = 30)
    #ax.tick_params(axis='both', which='major', labelsize=25)
    plt.ylabel('Number of observations - O3', fontsize = 30, fontweight = 'bold')    

    plt.legend(loc='upper right', prop={'size': 7})
    fig_title='Diurnal_'+place
    plt.savefig(fig_title, format='png',dpi=500, bbox_inches="tight")

    print ('saved=', fig_title)
    plt.show()


The header looks like this:
                             date_utc                       date parameter  \
    212580  {utc=2020-01-05T05:45:00.000Z  2020-01-05T11:15:00+05:30        o3   
    212581  {utc=2020-01-05T05:45:00.000Z  2020-01-05T11:15:00+05:30        o3   
    212582  {utc=2020-01-05T05:45:00.000Z  2020-01-05T11:15:00+05:30        o3   
    212583  {utc=2020-01-05T05:45:00.000Z  2020-01-05T11:15:00+05:30        o3   
    212584  {utc=2020-01-05T05:45:00.000Z  2020-01-05T11:15:00+05:30        o3   

                                               location  value   unit       city  \
    212580        ICRISAT Patancheru, Mumbai - TSPCB   37.7  µg/m³  Hyderabad   
    212581  Bollaram Industrial Area, Surat - TSPCB   39.5  µg/m³  Hyderabad   
    212582          IDA Pashamylaram, Surat - TSPCB   17.8  µg/m³  Hyderabad   
    212583               Sanathnagar, Hyderabad - TSPCB   56.6  µg/m³  Hyderabad   
    212584                  Zoo Park, Hyderabad - TSPCB   24.5  µg/m³  Hyderabad   

推荐答案

由于我无法完全重现您的示例，因此我根据自己的理解实施了一个玩具示例.请告诉我我是否理解错了.这是我的代码:

Since I was not able to fully reproduce your example, I implemented a toy example from what I understood. Please tell me if I understood something wrong. Here is my code:

import seaborn as sns
import numpy as np
import pandas as pd


df = pd.DataFrame([['Mumbai',2017,10],['Mumbai',2017,12],['Mumbai',2018,20],['Mumbai',2018,23],['Abu Dhabi',2017,30],['Abu Dhabi', 2018,25]], columns =['Place','Year','Amount'])

df_grouped = df.groupby(['Place','Year']).agg({'Amount':'count'}).reset_index()

sns.barplot(x='Place',y='Amount',hue='Year',data= df_grouped)

此代码将显示一个条形图，其中每个位置均位于x轴上，其总计数位于y轴上.此外，每个独特的年份将在条形图中获得自己的条形图.像这样:

This code will show a barplot, where each location will reside in x-axis and their total counts in y-axis. Moreover, each unique year will get its own bar in the barplot. Like this:

这篇关于经过多年的迭代以绘制不同的组值作为 pandas 中的条形图的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！