本文共 7433 字，大约阅读时间需要 24 分钟。

源数据（前12）

数据预处理（DATE格式转换）：

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

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

绘制折线图：

import matplotlib.pyplot as plt

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

x坐标标注旋转

plt.xticks(rotation = 45)

坐标轴与标题标注

plt.xlabel("Month") plt.ylabel("Unemployment Rate")plt.title("Monthly Unemployment Trend,1948")

子图操作：

fig = plt.figure()#新建绘图区域ax1 = fig.add_subplot(4,3,1)ax2 = fig.add_subplot(4,3,2)ax3 = fig.add_subplot(4,3,6)

fig = plt.figure(figsize=(6,6))#指定画图区域大小

ax1 = fig.add_subplot(2,1,1)ax2 = fig.add_subplot(2,1,2)ax1.plot(np.arange(5),np.random.randint(1,5,5))ax2.plot(np.arange(10),np.arange(10)*3)plt.show()

同一坐标系下绘制多条线：

fig = 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()

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()

曲线标签：

fig = plt.figure(figsize=(5,3))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')#标签定位#print (help(plt.legend))plt.show()

loc=  best	upper right	upper left	lower left	lower right	right	center left	center right	lower center	upper center	center

完整折线图： fig = plt.figure(figsize=(5,3)) 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()

绘制条形图：

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[:1])

FILM  RT_user_norm  Metacritic_user_nom  \0  Avengers: Age of Ultron (2015)           4.3                 3.55      IMDB_norm  Fandango_Ratingvalue  Fandango_Stars  0        3.9                   4.5             5.0

import matplotlib.pyplot as plt

from numpy import arange#The Axes.bar() method has 2 required parameters, left and height. #We use the left parameter to specify the x coordinates of the left sides of the bar. #We use the height parameter to specify the height of each barnum_cols = ['RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars']bar_heights = norm_reviews.ix[0, num_cols].valuesprint (bar_heights)bar_positions = arange(5) + 0.75print (bar_positions)fig, ax = plt.subplots()ax.bar(bar_positions, bar_heights, 0.5)plt.show()

[4.3 3.55 3.9 4.5 5.0][0.75 1.75 2.75 3.75 4.75]

横向条形图：

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()

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()

柱形图：

import pandas as pdimport matplotlib.pyplot as pltreviews = pd.read_csv('fandango_scores.csv')cols = ['FILM', 'RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue']norm_reviews = reviews[cols]print(norm_reviews[:5])

FILM  RT_user_norm  Metacritic_user_nom  \0  Avengers: Age of Ultron (2015)           4.3                 3.55   1               Cinderella (2015)           4.0                 3.75   2                  Ant-Man (2015)           4.5                 4.05   3          Do You Believe? (2015)           4.2                 2.35   4   Hot Tub Time Machine 2 (2015)           1.4                 1.70      IMDB_norm  Fandango_Ratingvalue  0       3.90                   4.5  1       3.55                   4.5  2       3.90                   4.5  3       2.70                   4.5  4       2.55                   3.0

fandango_distribution = norm_reviews['Fandango_Ratingvalue'].value_counts() fandango_distribution = fandango_distribution.sort_index() imdb_distribution = norm_reviews['IMDB_norm'].value_counts() imdb_distribution = imdb_distribution.sort_index() print(fandango_distribution) print(imdb_distribution)

2.7     22.8     22.9     53.0     43.1     33.2     53.3     43.4     93.5     93.6     83.7     93.8     53.9    124.0     74.1    164.2    124.3    114.4     74.5     94.6     44.8     3Name: Fandango_Ratingvalue, dtype: int642.00     12.10     12.15     12.20     12.30     22.45     22.50     12.55     12.60     22.70     42.75     52.80     22.85     12.90     12.95     33.00     23.05     43.10     13.15     93.20     63.25     43.30     93.35     73.40     13.45     73.50     43.55     73.60    103.65     53.70     83.75     63.80     33.85     43.90     93.95     24.00     14.05     14.10     44.15     14.20     24.30     1Name: IMDB_norm, dtype: int64

fig, ax = plt.subplots() #ax.hist(norm_reviews['Fandango_Ratingvalue'])#绘制柱形图 #ax.hist(norm_reviews['Fandango_Ratingvalue'],bins=20)#规定20条 ax.hist(norm_reviews['Fandango_Ratingvalue'], range=(4, 5),bins=20)#4到5范围内20条 plt.show()

fig = plt.figure(figsize=(5,20))

ax1 = fig.add_subplot(4,1,1)ax2 = fig.add_subplot(4,1,2)ax3 = fig.add_subplot(4,1,3)ax4 = fig.add_subplot(4,1,4)ax1.hist(norm_reviews['Fandango_Ratingvalue'], bins=20, range=(0, 5))ax1.set_title('Distribution of Fandango Ratings')ax1.set_ylim(0, 50)#y轴范围plt.show()

箱型图：

fig, ax = plt.subplots()ax.boxplot(norm_reviews['RT_user_norm'].values)ax.set_xticklabels(['Rotten Tomatoes'])ax.set_ylim(0, 5)plt.show()

num_cols = ['RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue']

fig, ax = plt.subplots()ax.boxplot(norm_reviews[num_cols].values)ax.set_xticklabels(num_cols, rotation=90)ax.set_ylim(0,5)plt.show()

去坐标锯齿：

fig, ax = plt.subplots()# Add your code here.fig, ax = plt.subplots()ax.tick_params(bottom="off", top="off", left="off", right="off")plt.show()

去边框：

fig, ax = plt.subplots()# Add your code here.fig, ax = plt.subplots()for key,spine in ax.spines.items():    spine.set_visible(False)plt.show()

RGB颜色通道：

cb_dark_blue = (0/255, 107/255, 164/255)

线宽：

ax.plot(women_degrees['Year'], women_degrees[major_cats[sp]], c=cb_dark_blue, label='Women', linewidth=10)

曲线标注：

ax.text(2005, 87, 'Men')ax.text(2002, 8, 'Women')