在窗口中组合多个图形

Question

我正在使用 kaggle dataframe 来练习使用 matplotlib。

我正在一张一张地创建条形图，但它不断增加。

当我调用 plt.show() 时，突然出现了大约 10 个图形窗口。

是否可以将其中 4 个图形合并到 1 个窗口中？

这些部分位于“时间分析”的同一部分，因此我想将这 4 个数字组合在 1 个窗口中。

import matplotlib.pyplot as plt
import seaborn as sns

dataset  = ('accidents_data.csv')
df = pd.read_csv(dataset)

"""Time Analysis :
Analyze the time that accidents happen for various patterns and trends"""
df.Start_Time = pd.to_datetime(df.Start_Time)  #convert the start time column to date time format
df['Hour_of_Accident'] = df.Start_Time.dt.hour #extract the hour from the time data

hour_accident = df['Hour_of_Accident'].value_counts()
hour_accident_df = hour_accident.to_frame() #convert the series data to dataframe in order to sort the index columns
hour_accident_df.index.names = ['Hours'] #naming the index column
hour_accident_df.sort_index(ascending=True, inplace=True)
print(hour_accident_df)

# Plotting the hour of accidents data in a bargraph
hour_accident_df.plot(kind='bar',figsize=(8,4),color='blue',title='Hour of Accident')
#plt.show() #Show the bar graph


"""Analyzing the accident frequency per day of the week"""
df['Day_of_the_week'] = df.Start_Time.dt.day_of_week
day_of_accident = df['Day_of_the_week'].value_counts()

day_of_accident_df = day_of_accident.to_frame() #convert the series data  to  dataframe so that we can sort the index columns
day_of_accident_df.index.names = ['Day'] # Renaming the index column
day_of_accident_df.sort_index(ascending=True, inplace=True)
print(day_of_accident_df)


f, ax = plt.subplots(figsize = (8, 5))
x = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Sartuday', 'Sunday']
l = day_of_accident_df.index.values
y = day_of_accident_df.Day_of_the_week

plt.bar(l, y, color='green')
plt.title('Day of the week vs total number of accidents')
plt.ylabel("No. of accidents recorded")
ax.set_xticks(l)
ax.set_xticklabels(x)
#plt.show()

"""Analysis for the months"""
df['Month'] = df.Start_Time.dt.month
accident_month = df['Month'].value_counts()

accident_month_df = accident_month.to_frame() #convert the series data  to  dataframe so that we can sort the index columns
accident_month_df.index.names = ['Month'] # Renaming the index column
accident_month_df.sort_index(ascending=True, inplace=True)
print(accident_month_df)

#Plotting the Bar Graph
accident_month_df.plot(kind='bar',figsize=(8,5),color='purple',title='Month of Accident')

"""Yearly Analysis"""
df['Year_of_accident'] = df.Start_Time.dt.year
#Check the yearly trend
yearly_count = df['Year_of_accident'].value_counts()
yearly_count_df = pd.DataFrame({'Year':yearly_count.index, 'Accidents':yearly_count.values})
yearly_count_df.sort_values(by='Year', ascending=True, inplace=True)
print(yearly_count_df)
#Creating line plot
yearly_count_df.plot.line(x='Year',color='red',title='Yearly Accident Trend ')

plt.show()