可视化分析库 matplotlib 基础01

Matplotlib库简介

Matplotlib 官网
Matplotlib 可能是 Python 2D- 绘图领域使用最广泛的套件。它能让使用者很轻松地将数据图形化，并且提供多样化的输出格式。通过数据绘图，我们可以将枯燥的数字转换成容易被人们接受的图表，从而让人留下更加深刻的印象。

matplotlib api 测试

绘制简单的折线图

import pandas as pdimport numpy as npimport matplotlib.pyplot as pltunrate = pd.read_csv("UNRATE.csv")unrate['DATE'] = pd.to_datetime(unrate['DATE'])print ("美国1948年各个月份失业率如下:\n", unrate.head(12))#指定x轴和y轴数据unrate_1948 = unrate.head(12)plt.plot(unrate_1948['DATE'], unrate_1948['VALUE'])#x轴数据值倾斜45显示plt.xticks(rotation=45)#指定x轴和y轴数据的标签plt.xlabel('Month')plt.ylabel('Unemployment Rate')#指定标题plt.title('Monthly Unemployment Trends, 1948')#是否显示辅助方格线plt.grid(False)plt.show()

绘制子图

import pandas as pdimport numpy as npimport matplotlib.pyplot as plt#创建画图区域，指定长和宽fig = plt.figure(figsize=(10,5))#画图区域指定在2*2矩阵中，第三个参数表示子图的位置ax1 = fig.add_subplot(2,2,1)ax2 = fig.add_subplot(2,2,2)#以[0,1,2,3,4]为横坐标值，以5个随机数组成的向量为纵坐标值画ax1子图ax1.plot(np.arange(5), np.random.randint(1,5,5), c='red')#以[0-2pi]为横坐标，以sin([0-2*pi]的值为纵坐标画ax2子图)ax2.plot(np.linspace(0,2*np.pi,8), np.sin(np.linspace(0,2*pi,8)), c='blue')plt.show()

循环绘制一张图表

#循环将1948-1952年的美国失业率情况绘制在统一图表中import pandas as pdimport numpy as npimport matplotlib.pyplot as pltunrate = pd.read_csv("UNRATE.csv")unrate['DATE'] = pd.to_datetime(unrate['DATE'])unrate['MONTH'] = unrate['DATE'].dt.monthfig = plt.figure(figsize=(8,4))colors = ['red', 'blue', 'green', 'orange', 'black']for i in range(5):    start_index = i*12    end_index = (i+1)*12    subset = unrate[start_index:end_index]    plt.plot(subset['MONTH'], subset['VALUE'], c=colors[i], label=str(1948+i))#显示图例在最佳位置    plt.legend(loc='best')#显示主方格辅助线plt.grid(True)#xy轴标签和图表标题plt.xlabel('Month, Integer')plt.ylabel('Unemployment Rate, Percent')plt.title('Monthly Unemployment Trends, 1948-1952')plt.show()

绘制一个纵向柱形图

#柱形图测试1import pandas as pdimport numpy as npimport matplotlib.pyplot as pltreviews = pd.read_csv("fandango_score_comparison.csv")cols = ['FILM', 'RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars']norm_reviews = reviews[cols]print ("样本数据如下:\n", norm_reviews[:3])num_cols = cols[1:]print ("第0号样本的电影打分情况：", norm_reviews.loc[0, num_cols].values)bar_heights = norm_reviews.loc[0, num_cols].valuesbar_positions = np.arange(5) + 1# 返回一个 (figure, ax) 的元组fig,ax = plt.subplots()# 绘制纵向柱形图ax.bar(bar_positions, bar_heights, 0.4) #0.4指定柱的宽度# 设置x轴bar的位置和x轴数据显示的角度ax.set_xticks(range(1,6))ax.set_xticklabels(num_cols, rotation=45)# 设置xy轴的标签和图表的标题ax.set_xlabel('Rating Source')ax.set_ylabel('Average Rating')ax.set_title('Average User Rating For Avengers: Age of Ultron (2015)')plt.show()

绘制一个横向柱形图

#柱形图测试2import pandas as pdimport numpy as npimport matplotlib.pyplot as pltreviews = pd.read_csv("fandango_score_comparison.csv")cols = ['FILM', 'RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars']norm_reviews = reviews[cols]num_cols = cols[1:]bar_heights = norm_reviews.loc[0, num_cols].valuesbar_positions = np.arange(5) + 1# 返回一个 (figure, ax) 的元组fig,ax = plt.subplots()ax.barh(bar_positions, bar_heights, 0.4)  ##### 设置y轴bar的位置和y轴数据显示的角度ax.set_yticks(range(1,6))# 绘制横向柱形图ax.set_yticklabels(num_cols, rotation=0)# 设置xy轴的标签和图表的标题ax.set_ylabel('Rating Source')ax.set_xlabel('Average Rating')ax.set_title('Average User Rating For Avengers: Age of Ultron (2015)')plt.show()

绘制散点图测试1

import pandas as pdimport numpy as npimport matplotlib.pyplot as pltreviews = pd.read_csv("fandango_score_comparison.csv")cols = ['FILM', 'RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars']norm_reviews = reviews[cols]fig,ax = plt.subplots()ax.scatter(norm_reviews['Fandango_Ratingvalue'], norm_reviews['RT_user_norm'])ax.set_xlabel('Fandango')ax.set_ylabel('Rotten Tomatoes')plt.show()

绘制散点图测试2

import pandas as pdimport numpy as npimport matplotlib.pyplot as pltreviews = pd.read_csv("fandango_score_comparison.csv")cols = ['FILM', 'RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars']norm_reviews = reviews[cols]fig = plt.figure(figsize=(10,10) )ax1 = fig.add_subplot(2,2,1)ax2 = fig.add_subplot(2,2,2)ax1.scatter(norm_reviews['Fandango_Ratingvalue'], norm_reviews['RT_user_norm'])ax1.set_xlabel('Fandango')ax1.set_ylabel('Rotten Tomatoes')ax2.scatter(norm_reviews['RT_user_norm'], norm_reviews['Fandango_Ratingvalue'])ax2.set_xlabel('Rotten Tomatoes')ax2.set_ylabel('Fandango')plt.show()

使用bins区域化x轴

# 使用bins区域化x轴# 如 将1,2,3,4,5,6,7,8的分布表示为 [1,3),[3,5),[5,7),[7,9)的分布import pandas as pdimport numpy as npimport matplotlib.pyplot as pltreviews = pd.read_csv("fandango_score_comparison.csv")cols = ['FILM', 'RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue']norm_reviews = reviews[cols]fandango_distribution = norm_reviews['Fandango_Ratingvalue'].value_counts() #计算对应值出现的次数fandango_distribution = fandango_distribution.sort_index() #按照数值的索引重新排序得到新的Seriesimdb_distribution = norm_reviews['IMDB_norm'].value_counts() imdb_distribution = imdb_distribution.sort_index() print ("测试数据fandango_distribution：\n", fandango_distribution.head(10))print ("测试数据imdb_distribution：\n", imdb_distribution.head(10))fig,ax = plt.subplots(figsize=(8,4))ax.hist(norm_reviews['Fandango_Ratingvalue'])ax.hist(norm_reviews['Fandango_Ratingvalue'], bins=20)ax.hist(norm_reviews['Fandango_Ratingvalue'], range=(4,5), bins=20)plt.show()

根据已有数据画一个盒图

import pandas as pdimport numpy as npimport matplotlib.pyplot as pltreviews = pd.read_csv("fandango_score_comparison.csv")num_cols = ['RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue']print ("测试数据示例如下：\n", reviews[num_cols].head(5))fig,ax = plt.subplots(figsize=(6,6))ax.boxplot(reviews[num_cols].values)ax.set_xticklabels(num_cols, rotation=90)ax.set_ylim(0,5) #设置y轴的取值范围plt.show()