Python可视化库Matplotlib的使用

一。导入数据

import pandas as pdunrate = pd.read_csv('unrate.csv')unrate['DATE'] = pd.to_datetime(unrate['DATE'])print(unrate.head(12))

 结果如下：        DATE  VALUE0  1948-01-01    3.41  1948-02-01    3.82  1948-03-01    4.03  1948-04-01    3.94  1948-05-01    3.55  1948-06-01    3.66  1948-07-01    3.67  1948-08-01    3.98  1948-09-01    3.89  1948-10-01    3.710 1948-11-01    3.811 1948-12-01    4.0二。使用Matplotlib库

import matplotlib.pyplot as plt#%matplotlib inline#Using the different pyplot functions, we can create, customize, and display a plot. For example, we can use 2 functions to :plt.plot()plt.show()

结果如下：

三。插入数据

first_twelve = unrate[0:12]plt.plot(first_twelve['DATE'], first_twelve['VALUE'])plt.show()

 

 由于x轴过于紧凑，所以使用旋转x轴的方法 结果如下。

plt.plot(first_twelve['DATE'], first_twelve['VALUE'])plt.xticks(rotation=45)#print help(plt.xticks)plt.show()

四。设置x轴y轴说明

plt.plot(first_twelve['DATE'], first_twelve['VALUE'])plt.xticks(rotation=90)plt.xlabel('Month')plt.ylabel('Unemployment Rate')plt.title('Monthly Unemployment Trends, 1948')plt.show()

五。子图设置

import matplotlib.pyplot as pltfig = plt.figure()ax1 = fig.add_subplot(4,3,1)ax2 = fig.add_subplot(4,3,2)ax2 = fig.add_subplot(4,3,6)plt.show()

 

 

 

 六。一个图标多个曲线。

1.简单实验。

unrate['MONTH'] = unrate['DATE'].dt.monthunrate['MONTH'] = unrate['DATE'].dt.monthfig = plt.figure(figsize=(6,3))plt.plot(unrate[0:12]['MONTH'], unrate[0:12]['VALUE'], c='red')plt.plot(unrate[12:24]['MONTH'], unrate[12:24]['VALUE'], c='blue')plt.show()

2.使用循环

fig = plt.figure(figsize=(10,6))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])   plt.show()

3.设置标签

fig = plt.figure(figsize=(10,6))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]    label = str(1948 + i)    plt.plot(subset['MONTH'], subset['VALUE'], c=colors[i], label=label)plt.legend(loc='best')#print help(plt.legend)plt.show()

 

 4。设置完整标签

fig = plt.figure(figsize=(10,6))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]    label = str(1948 + i)    plt.plot(subset['MONTH'], subset['VALUE'], c=colors[i], label=label)plt.legend(loc='upper left')plt.xlabel('Month, Integer')plt.ylabel('Unemployment Rate, Percent')plt.title('Monthly Unemployment Trends, 1948-1952')plt.show()

 七。折线图（某电影评分网站）

1.读取数据

import pandas as pdreviews = pd.read_csv('fandango_scores.csv')cols = ['FILM', 'RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars']norm_reviews = reviews[cols]print(norm_reviews[:10])

  2.设置说明

num_cols = ['RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars']bar_heights = norm_reviews.ix[0, num_cols].valuesbar_positions = arange(5) + 0.75tick_positions = range(1,6)fig, ax = plt.subplots()ax.bar(bar_positions, bar_heights, 0.5)//ax.bar绘制折线图，bar_positions绘制离远点的距离，0.5绘制离折线图的宽度。ax.set_xticks(tick_positions)ax.set_xticklabels(num_cols, rotation=45)//横轴的说明 旋转45度 横轴说明ax.set_xlabel('Rating Source')ax.set_ylabel('Average Rating')ax.set_title('Average User Rating For Avengers: Age of Ultron (2015)')plt.show()

3.旋转x轴 y轴

import matplotlib.pyplot as pltfrom numpy import arangenum_cols = ['RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars']bar_widths = norm_reviews.ix[0, num_cols].valuesbar_positions = arange(5) + 0.75tick_positions = range(1,6)fig, ax = plt.subplots()ax.barh(bar_positions, bar_widths, 0.5)ax.set_yticks(tick_positions)ax.set_yticklabels(num_cols)ax.set_ylabel('Rating Source')ax.set_xlabel('Average Rating')ax.set_title('Average User Rating For Avengers: Age of Ultron (2015)')plt.show()

 

八。 散点图

1。基本散点图

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()

2.拆分散点图

#Switching Axesfig = plt.figure(figsize=(5,10))ax1 = fig.add_subplot(2,1,1)ax2 = fig.add_subplot(2,1,2)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()

Ps:还是呈现很强的相关性的，基本呈直线分布