标签：sign spin 定义 sci meta use params count ems
1.绘制折线图
在pandas里面有一种数据类型为datatime ，可以将不规范的日期改为：xxxx-xx-xx
import pandas as pd import numpy as np a = pd.read_csv(‘UNRATE.csv‘) a[‘DATE‘] = pd.to_datetime(a[‘DATE‘]) print(a.head(12))
折线图
import pandas as pd import numpy as np import matplotlib.pyplot as plt a = pd.read_csv(‘UNRATE.csv‘) b = a[0:12] plt.plot(b[‘DATE‘],b[‘VALUE‘]) plt.show()
这样就能绘制出一个折线图了
如果横坐标写不下怎么办？我们可以将文字竖着写或者指定一个角度
plt.xticks(rotation = 45) #其中的45表示45°（和数学里面一样）
一般情况下要写横坐标与纵坐标要表达什么，还有标题
import pandas as pd import numpy as np import matplotlib.pyplot as plt a = pd.read_csv(‘UNRATE.csv‘) #导入文件 b = a[0:12] #将数据的前12条提取出来 plt.plot(b[‘DATE‘],b[‘VALUE‘]) #导入横纵坐标的数据 plt.xticks(rotation = 90) #横坐标90 plt.xlabel(‘Month‘) #横坐标名称 plt.ylabel(‘Unemployment Rate‘) #纵坐标名称 plt.title(‘Monthly Unemployment Trends, 1948‘) #标题 plt.show() #展示
输出;
unrate[‘MONTH‘] = unrate[‘DATE‘].dt.month unrate[‘MONTH‘] = unrate[‘DATE‘].dt.month fig = plt.figure(figsize=(6,3)) #图的大小 plt.plot(unrate[0:12][‘MONTH‘], unrate[0:12][‘VALUE‘], c=‘red‘) #c为颜色 plt.plot(unrate[12:24][‘MONTH‘], unrate[12:24][‘VALUE‘], c=‘blue‘) #在同一张图上绘制两条折线并进行对比 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]) #绘制5条折线在一张图中，用颜色加以区分 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] label = str(1948 + i) plt.plot(subset[‘MONTH‘], subset[‘VALUE‘], c=colors[i], label=label) plt.legend(loc=‘best‘) #legend表示添加图例，loc是图例在折线图中的位置，best表示在系统觉得合适的位置，当然也可以自定义位置，位置的选择请help（legend） #print help(plt.legend) plt.show()
输出：
最终版：
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] #数据区间 label = str(1948 + i) #图例每次写的折线标题 plt.plot(subset[‘MONTH‘], subset[‘VALUE‘], c=colors[i], label=label) plt.legend(loc=‘upper left‘) #放到左上角 plt.xlabel(‘Month, Integer‘) #横坐标标题 plt.ylabel(‘Unemployment Rate, Percent‘) #纵坐标标题 plt.title(‘Monthly Unemployment Trends, 1948-1952‘) #折线图标题 plt.show()
输出：
3、条形图与散点图
import pandas as pd import numpy as np from numpy import arange import matplotlib.pyplot as plt 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] num_cols = [‘RT_user_norm‘, ‘Metacritic_user_nom‘, ‘IMDB_norm‘, ‘Fandango_Ratingvalue‘, ‘Fandango_Stars‘] bar_heights = norm_reviews.ix[0, num_cols].values #当前柱的高度 #print bar_heights bar_positions = arange(5) + 0.75 #0.75是第一个柱离原点的距离 然后每个柱距离为1 一共5个柱 #print bar_positions fig, ax = plt.subplots() ax.bar(bar_positions, bar_heights, 0.5) #0.5表示柱子的宽度 plt.show()
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.set_xticks(tick_positions) ax.set_xticklabels(num_cols, rotation=45) ax.set_xlabel(‘Rating Source‘) #横坐标 ax.set_ylabel(‘Average Rating‘) #纵坐标 ax.set_title(‘Average User Rating For Avengers: Age of Ultron (2015)‘) #标题
plt.show()
输出：
当然，也可以将柱形图变为横着的
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) #需要改变的地方，将bar改为barh 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()
输出：
散点图：
fig, ax = plt.subplots() ax.scatter(norm_reviews[‘Fandango_Ratingvalue‘], norm_reviews #scatter画散点图 [‘RT_user_norm‘]) ax.set_xlabel(‘Fandango‘) ax.set_ylabel(‘Rotten Tomatoes‘) plt.show()
输出：
画两个散点图：
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()
输出：
用fig设置参数，ax做实际画图的操作
4、柱形图与盒图
求数据的频数，并可视化
import pandas as pd import numpy as np from numpy import arange 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[:5]) #输出数据 fandango_distribution = norm_reviews[‘Fandango_Ratingvalue‘].value_counts() #需要数据 fandango_distribution = fandango_distribution.sort_index() #从小到大排序 imdb_distribution = norm_reviews[‘IMDB_norm‘].value_counts() imdb_distribution = imdb_distribution.sort_index() print(fandango_distribution) #一组数据的频数，比如4.3出现了6次 表示为：4.3 6 print(imdb_distribution) #另一组数据的频数 fig, ax = plt.subplots() ax.hist(norm_reviews[‘Fandango_Ratingvalue‘]) #画出柱形图 #ax.hist(norm_reviews[‘Fandango_Ratingvalue‘],bins=20) #bins = 20 表示一共有20个柱子 #ax.hist(norm_reviews[‘Fandango_Ratingvalue‘], range=(4, 5),bins=20) #range代表了横坐标的区间 plt.show()
import pandas as pd import numpy as np from numpy import arange 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] 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) #指定了这组数据的y轴取值区间 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()
输出：（在ml里run一下，太长了）
盒图（四分图，找中位数）：
import pandas as pd import numpy as np from numpy import arange 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] fig, ax = plt.subplots() ax.boxplot(norm_reviews[‘RT_user_norm‘]) ax.set_xticklabels([‘Rotten Tomatoes‘]) ax.set_ylim(0, 5) plt.show()
输出：
这样，就可以清晰的看到中位数的位置以及大致的数据区间
也可以在一张图上放入多张盒图，这样就可以区分各个属性的特征了
import pandas as pd import numpy as np from numpy import arange 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] 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()
输出：
5、闲的蛋疼系列：
可以将坐标轴去掉：
for key,spine in ax.spines.items(): spine.set_visible(False) #去掉横纵坐标轴的线
可以去掉坐标轴的锯齿：
ax.tick_params(bottom="off", top="off", left="off", right="off")
6、最后的一些方法
*****一般在做图时为了让图中表达的清晰，让图尽量在一行或两行
fig = plt.figure(figsize=(12, 12)) #figsize参数调试
在作图时的颜色可以用自己定义的颜色
#Color import pandas as pd import matplotlib.pyplot as plt women_degrees = pd.read_csv(‘percent-bachelors-degrees-women-usa.csv‘) major_cats = [‘Biology‘, ‘Computer Science‘, ‘Engineering‘, ‘Math and Statistics‘] cb_dark_blue = (0/255, 107/255, 164/255) #自定义颜色，注意格式 cb_orange = (255/255, 128/255, 14/255) fig = plt.figure(figsize=(12, 12)) for sp in range(0,4): ax = fig.add_subplot(2,2,sp+1) # The color for each line is assigned here. ax.plot(women_degrees[‘Year‘], women_degrees[major_cats[sp]], c=cb_dark_blue, label=‘Women‘) ax.plot(women_degrees[‘Year‘], 100-women_degrees[major_cats[sp]], c=cb_orange, label=‘Men‘) for key,spine in ax.spines.items(): spine.set_visible(False) ax.set_xlim(1968, 2011) ax.set_ylim(0,100) ax.set_title(major_cats[sp]) ax.tick_params(bottom="off", top="off", left="off", right="off") plt.legend(loc=‘upper right‘) plt.show()
如果要让线的宽度改变，让
ax.plot(women_degrees[‘Year‘], women_degrees[major_cats[sp]], c=cb_dark_blue, label=‘Women‘, linewidth=10) #linewidth是改变线宽度的参数 ax.plot(women_degrees[‘Year‘], 100-women_degrees[major_cats[sp]], c=cb_orange, label=‘Men‘, linewidth=10)
最终附上一波此例完整版：（其中有在图中某一坐标上标出此点名称）：
import pandas as pd import numpy as np from numpy import arange import matplotlib.pyplot as plt women_degrees = pd.read_csv(‘percent-bachelors-degrees-women-usa.csv‘) major_cats = [‘Biology‘, ‘Computer Science‘, ‘Engineering‘, ‘Math and Statistics‘] stem_cats = [‘Engineering‘, ‘Computer Science‘, ‘Psychology‘, ‘Biology‘, ‘Physical Sciences‘, ‘Math and Statistics‘] cb_dark_blue = (0/255, 107/255, 164/255) cb_orange = (255/255, 128/255, 14/255) fig = plt.figure(figsize=(18, 3)) for sp in range(0, 6): ax = fig.add_subplot(1, 6, sp + 1) ax.plot(women_degrees[‘Year‘], women_degrees[stem_cats[sp]], c=cb_dark_blue, label=‘Women‘, linewidth=3) ax.plot(women_degrees[‘Year‘], 100 - women_degrees[stem_cats[sp]], c=cb_orange, label=‘Men‘, linewidth=3) for key, spine in ax.spines.items(): spine.set_visible(False) ax.set_xlim(1968, 2011) ax.set_ylim(0, 100) ax.set_title(stem_cats[sp]) ax.tick_params(bottom="off", top="off", left="off", right="off") plt.legend(loc=‘upper right‘) plt.show() fig = plt.figure(figsize=(18, 3)) for sp in range(0, 6): ax = fig.add_subplot(1, 6, sp + 1) ax.plot(women_degrees[‘Year‘], women_degrees[stem_cats[sp]], c=cb_dark_blue, label=‘Women‘, linewidth=3) ax.plot(women_degrees[‘Year‘], 100 - women_degrees[stem_cats[sp]], c=cb_orange, label=‘Men‘, linewidth=3) for key, spine in ax.spines.items(): spine.set_visible(False) ax.set_xlim(1968, 2011) ax.set_ylim(0, 100) ax.set_title(stem_cats[sp]) ax.tick_params(bottom="off", top="off", left="off", right="off") if sp == 0: #设置if语句后会对需要的图上加点的名称 ax.text(2005, 87, ‘Men‘) #在坐标（2005，87）处标men ax.text(2002, 8, ‘Women‘) elif sp == 5: ax.text(2005, 62, ‘Men‘) ax.text(2001, 35, ‘Women‘) plt.show()
输出：
标签：sign spin 定义 sci meta use params count ems
原文地址：https://www.cnblogs.com/ggnbnb/p/9823276.html
