用matplotlib對數據可視化

下圖是要用到的數據集，反映了從1984到2016年的失業率的變化

 

 

　1.導入可視化模塊import matlibplot.pyplot as plt， 函數plt.plot(x, y)肯定折線圖的點,x是由這些點的x座標組成的列表，y是由這些點的y座標組成

的列表。plt.show()顯示圖像，plt.xlabel()給x軸命名，plt.xticks()能夠設置x座標刻度點旋轉指定角度，plt.title()給折線圖命名

　　下面的代碼是以上函數的應用

 1 import pandas as pd
 2 import numpy as np
 3 import matplotlib.pyplot as plt
 4 #畫出1984年失業率折線圖
 5 unrated = pd.read_csv("C:/學習/python/hello/UNRATE.csv")
 6 first_twelve = unrated.head(12)
 7 
 8 x_series = first_twelve["DATE"]
 9 y_series = first_twelve["VALUE"]
10 
11 plt.xticks(rotation=90)
12 plt.xlabel("Month")
13 plt.ylabel("Unemployment rate")
14 
15 plt.title("Unemployment rate trends on 1984")
16 
17 plt.plot(x_series, y_series)
18 plt.show()

　　運行結果以下

 

2.經過plt.subplot(n, m, x)在一個figure中添加多個子圖, n和m表示子圖的佈局，分別表明行數和列數，x表示從左往右，從上往下數的第x個子圖

下面的代碼提供了該函數使用實例

 1 import pandas as pd
 2 import numpy as np
 3 import matplotlib.pyplot as plt
 4 
 5 plt.figure(figsize=(20, 16))
 6 ax1 = plt.subplot(2, 3, 1)
 7 ax2 = plt.subplot(2, 3, 2)
 8 ax3 = plt.subplot(2, 3, 3)
 9 ax5 = plt.subplot(2, 3, 5)
10 plt.show()

運行結果以下

3.下面的代碼是在一個座標軸中畫多個折線圖的示例

 1 import pandas as pd
 2 import numpy as np
 3 import matplotlib.pyplot as plt
 4 
 5 unrated = pd.read_csv("C:/學習/python/hello/UNRATE.csv")
 6 unrated["DATE"] = pd.to_datetime(unrated["DATE"])
 7 color = ["red", "yellow", "blue", "green", "purple"]
 8 plt.figure(figsize=(20, 16))
 9 for i in range(5):
10     sub_unrated = unrated.loc[i*12:(i+1)*12-1]
11     sub_unrated_x = sub_unrated['DATE'].dt.month
12     sub_unrated_y = sub_unrated["VALUE"]
13     label = 1948+i
14     plt.plot(sub_unrated_x, sub_unrated_y, color=color[i], label=label)
15 plt.legend(loc="best")
16 plt.show()

運行結果以下

 

 4.figure和subplot的定義順序決定了subplot是畫在哪一個figure中。當代碼中定義了多個figure時候，緊接着該figure定義的subplot才畫在該figure中，

以下代碼所示，定義了figure1和figure2，ax1和ax2在figure1中，ax在figure2中。

1 plt.figure(figsize=(20, 16))
2 ax1 = plt.subplot(2,2,1)
3 plt.figure(figsize=(14, 12))
4 ax = plt.subplot(1, 1, 1)
5 
6 plt.show()

5.用matplotlib畫條形圖

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

reviews = pd.read_csv("C:/學習/python/hello/fandango_score_comparison.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_height = norm_reviews.loc[0, num_cols].values  #第一部電影的評價，注意利用loc索引某一行的用法，能夠添加第二維

bar_position = 1 + np.arange(5)  #arange返回的是ndarray類型，range返回的是list類，使用arange須要用numpy
plt.figure(figsize=(10, 10))
ax = plt.subplot(1, 1, 1)
ax.bar(bar_position, bar_height, 0.3)  #bar_position是條形圖的x座標(中點座標),bar_height是高，0.3是寬
#設置x座標刻度
tick_positions = range(1, 6)
ax.set_xticks(tick_positions)
ax.set_xticklabels(num_cols)

#設置x軸和y軸名稱
ax.set_xlabel("Rating Source")
ax.set_ylabel("Average Rating")
ax.set_title("Whatever")
plt.show()

運行結果以下

　　將上面的代碼改變幾處，就會成爲橫着的條形圖了。代碼以下所示（改動之處用白底紅字加粗下劃線標出來了）

 1 import pandas as pd
 2 import numpy as np
 3 import matplotlib.pyplot as plt
 4 
 5 reviews = pd.read_csv("C:/學習/python/hello/fandango_score_comparison.csv")
 6 cols = ["FILM", "RT_user_norm", "Metacritic_user_nom", "IMDB_norm", "Fandango_Ratingvalue", "Fandango_Stars"]
 7 norm_reviews = reviews[cols]
 8 
 9 num_cols = ["RT_user_norm", "Metacritic_user_nom", "IMDB_norm", "Fandango_Ratingvalue", "Fandango_Stars"]
10 bar_height = norm_reviews.loc[0, num_cols].values  #第一部電影的評價，注意利用loc索引某一行的用法，能夠添加第二維
11 
12 bar_position = 1 + np.arange(5)  #arange返回的是ndarray類型，range返回的是list類，使用arange須要用numpy
13 plt.figure(figsize=(10, 10))
14 ax = plt.subplot(1, 1, 1)
15 ax.barh(bar_position, bar_height, 0.3)  #bar_position是條形圖的x座標(中點座標),bar_height是高，0.3是寬
16 #設置x座標刻度
17 tick_positions = range(1, 6)
18 ax.set_yticks(tick_positions)
19 ax.set_yticklabels(num_cols)
20 
21 #設置x軸和y軸名稱
22 ax.set_ylabel("Rating Source")
23 ax.set_xlabel("Average Rating")
24 ax.set_title("Whatever")
25 plt.show()

運行結果以下

6.畫散點圖

1 import pandas as pd
2 import numpy as np
3 import matplotlib.pyplot as plt
4 
5 reviews = pd.read_csv("C:/學習/python/hello/fandango_score_comparison.csv")
6 plt.figure(figsize=(10,10))
7 ax = plt.subplot(1, 1, 1)
8 ax.scatter(reviews["RT_norm"], reviews["Metacritic_user_nom"])
9 plt.show()

運行結果以下

7.設a是Series類型，b = a.value_counts()能夠獲得a的一個頻數統計，b是Series結構，b的index是a的值，b的value是該值出現的頻數。

以下代碼所示

 1 import pandas as pd
 2 import numpy as np
 3 import matplotlib.pyplot as plt
 4 
 5 reviews = pd.read_csv("C:/學習/python/hello/fandango_score_comparison.csv")
 6 cols = ["FILM", "RT_user_norm", "Metacritic_user_nom", "Fandango_Ratingvalue"]
 7 norm_reviews = reviews[cols]
 8 #fandango_distribution是Series結構，index是原來的列的值，value是該值出現的頻率
 9 fandango_distribution = norm_reviews["Fandango_Ratingvalue"].value_counts()
10 print(fandango_distribution.head(5))
11 print(type(fandango_distribution))
12 print(fandango_distribution.index)

運行結果以下

8.咱們來畫直方圖

 1 import pandas as pd
 2 import numpy as np
 3 import matplotlib.pyplot as plt
 4 
 5 reviews = pd.read_csv("C:/學習/python/hello/fandango_score_comparison.csv")
 6 cols = ["FILM", "RT_user_norm", "Metacritic_user_nom","IMDB_norm", "Fandango_Ratingvalue"]
 7 norm_reviews = reviews[cols]
 8 plt.figure(figsize=(10, 10))
 9 ax = plt.subplot(1, 1, 1)
10 ax.hist(norm_reviews["RT_user_norm"], bins=20) #參數bins表示直方圖的x軸分紅多少區間
11 
12 plt.show()

運行結果以下