一。导入数据
import pandas as pd
unrate = pd.read_csv('unrate.csv')
unrate['DATE'] = pd.to_datetime(unrate['DATE'])
print(unrate.head(12))
结果如下：
DATE VALUE
0 1948-01-01 3.4
1 1948-02-01 3.8
2 1948-03-01 4.0
3 1948-04-01 3.9
4 1948-05-01 3.5
5 1948-06-01 3.6
6 1948-07-01 3.6
7 1948-08-01 3.9
8 1948-09-01 3.8
9 1948-10-01 3.7
10 1948-11-01 3.8
11 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 plt
fig = 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.month
unrate['MONTH'] = unrate['DATE'].dt.month
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()
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 pd
reviews = 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].values
bar_positions = arange(5) + 0.75
tick_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 plt
from numpy import arange
num_cols = ['RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars'] bar_widths = norm_reviews.ix[0, num_cols].values
bar_positions = arange(5) + 0.75
tick_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 Axes
fig = 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
九。直方图
1.读入数据
import pandas as pd
import matplotlib.pyplot as plt
reviews = 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[:100])
2.统计评分个数
fandango_distribution = norm_reviews['Fandango_Ratingvalue'].value_counts()//统计
fandango_distribution = fandango_distribution.sort_index()//排序 imdb_distribution = norm_reviews['IMDB_norm'].value_counts()
imdb_distribution = imdb_distribution.sort_index() print(fandango_distribution)
print(imdb_distribution)
3.画直方图
fig, ax = plt.subplots()
#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)//划分的区间20个，只统计4-5区间的bins
plt.show()
4.不同的媒体评分图
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) ax2.hist(norm_reviews['RT_user_norm'], 20, range=(0, 5))
ax2.set_title('Distribution of Rotten Tomatoes Ratings')
ax2.set_ylim(0, 50) ax3.hist(norm_reviews['Metacritic_user_nom'], 20, range=(0, 5))
ax3.set_title('Distribution of Metacritic Ratings')
ax3.set_ylim(0, 50) ax4.hist(norm_reviews['IMDB_norm'], 20, range=(0, 5))
ax4.set_title('Distribution of IMDB Ratings')
ax4.set_ylim(0, 50) plt.show()
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*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" alt="" />
5.四分图
fig, ax = plt.subplots()
ax.boxplot(norm_reviews['RT_user_norm'])
ax.set_xticklabels(['Rotten Tomatoes'])
ax.set_ylim(0, 5)
plt.show()
ps：四分图就是1/4,2/4,3/4的点是多少，可以看到大致的范围
6.四家媒体四方图
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)//打分范围
plt.show()
