Day 21 [Python ML、资料视觉化] 长条图和热度图

设定jupyter notebook

import pandas as pd
pd.plotting.register_matplotlib_converters()
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
print("Setup Complete")

Setup Complete

读取资料

我们使用pd.read_csv来读取档案

# Path of the file to read
flight_filepath = "./flight_delays.csv"

# Read the file into a variable flight_data
flight_data = pd.read_csv(flight_filepath, index_col="Month")

因为month这个column并不是日期的格式，因此在读取的时候，不用加上parse_dates=True

显示资料

因为资料集很小，可以只用一行就印出全部的资料

# Print the data
flight_data

长条图

将Airline code NK的班机做成bar chart

# Set the width and height of the figure
plt.figure(figsize=(10,6))

# Add title
plt.title("Average Arrival Delay for Spirit Airlines Flights, by Month")

# Bar chart showing average arrival delay for Spirit Airlines Flights by month
sns.barplot(x=flight_data.index, y=flight_data['NK'])

# Add label for vertical axis
plt.ylabel("Arrival delay (minutes)")

Text(0, 0.5, 'Arrival delay (minutes)')

在bar chart中，有几个主要的components

sns.barplot - 告诉Notebook要创造一个bar chart
x=flight_data.index - 选择index当作x轴的资料
y=flight_data['NK'] - 要用什麽资料来当作bar chart的高度
**Important Note:**若x已经设定index，index是用month，因此y轴不能使用flight_data['month']

热度图

下方的程序码是将flight_data的资料做成heatmap

# Set the width and height of the figure
plt.figure(figsize=(14,7))

# Add title
plt.title("Average Arrival Delay for Each Airline, by Month")

# Heatmap showing average arrival delay for each airline by month
sns.heatmap(data=flight_data, annot=True)

# Add label for horizontal axis
plt.xlabel("Airline")