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AI基础：Pandas简易入门

發布時間：2025/3/8 ChatGpt 27 豆豆

生活随笔收集整理的這篇文章主要介紹了 AI基础：Pandas简易入门小編覺得挺不錯的,現在分享給大家,幫大家做個參考.

Pandas 簡易入門

0.導語

pandas 是基于 NumPy 的一種工具，該工具是為了解決數據分析任務而創建的。Pandas 納入了大量庫和一些標準的數據模型，提供了高效地操作大型數據集所需的工具。pandas 提供了大量能使我們快速便捷地處理數據的函數和方法。你很快就會發現，它是使 Python 成為強大而高效的數據分析環境的重要因素之一。

在此之前，我已經寫了一篇Numpy的快速入門：

AI基礎：Numpy簡易入門，建議先看這篇。

接下來怎么快速入門Pandas？

先完整運行本文的代碼，預計用一天時間就夠了，再嘗試完成這篇文章的代碼：

Pandas 練習題-提高你的數據分析技能，鞏固下。

接著呢？可以練習這篇文章的代碼：

學完可以解決 90%以上的數據分析問題-利用 python 進行數據分析第二版（代碼和中文筆記），有了前面的基礎，看起來會非常快，這個時候，pandas的基本操作都會了。

備注：本文代碼可以在github下載

https://github.com/fengdu78/Data-Science-Notes/tree/master/3.pandas

1.Series

import pandas as pd import numpy as np# Series s = pd.Series([1,3,6,np.nan,44,1]) print(s) # 默認index從0開始,如果想要按照自己的索引設置，則修改index參數,如:index=[3,4,3,7,8,9] 0 1.0 1 3.0 2 6.0 3 NaN 4 44.0 5 1.0 dtype: float64

2.DataFrame

2.1 DataFrame 的簡單運用

# DataFrame dates = pd.date_range('2018-08-19',periods=6) # dates = pd.date_range('2018-08-19','2018-08-24') # 起始、結束與上述等價 ''' numpy.random.randn(d0, d1, …, dn)是從標準正態分布中返回一個或多個樣本值。 numpy.random.rand(d0, d1, …, dn)的隨機樣本位于[0, 1)中。 (6,4)表示6行4列數據 ''' df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=['a','b','c','d']) print(df) # DataFrame既有行索引也有列索引，它可以被看做由Series組成的大字典。 a b c d 2018-08-19 0.090400 -0.029562 -2.004038 2.686679 2018-08-20 -0.531038 -0.750023 0.662672 1.637006 2018-08-21 -1.040762 -0.005521 -0.531630 0.192298 2018-08-22 -0.388458 0.456383 0.412524 1.918840 2018-08-23 0.446538 1.062472 1.179866 -0.725910 2018-08-24 2.828722 1.234659 1.251329 0.620756 print(df['b']) 2018-08-19 -0.029562 2018-08-20 -0.750023 2018-08-21 -0.005521 2018-08-22 0.456383 2018-08-23 1.062472 2018-08-24 1.234659 Freq: D, Name: b, dtype: float64 # 未指定行標簽和列標簽的數據 df1 = pd.DataFrame(np.arange(12).reshape(3,4)) print(df1) 0 1 2 3 0 0 1 2 3 1 4 5 6 7 2 8 9 10 11 # 另一種方式 df2 = pd.DataFrame({'A': [1,2,3,4],'B': pd.Timestamp('20180819'),'C': pd.Series([1,6,9,10],dtype='float32'),'D': np.array([3] * 4,dtype='int32'),'E': pd.Categorical(['test','train','test','train']),'F': 'foo' }) print(df2) A B C D E F 0 1 2018-08-19 1.0 3 test foo 1 2 2018-08-19 6.0 3 train foo 2 3 2018-08-19 9.0 3 test foo 3 4 2018-08-19 10.0 3 train foo print(df2.index) RangeIndex(start=0, stop=4, step=1) print(df2.columns) Index(['A', 'B', 'C', 'D', 'E', 'F'], dtype='object') print(df2.values) [[1 Timestamp('2018-08-19 00:00:00') 1.0 3 'test' 'foo'][2 Timestamp('2018-08-19 00:00:00') 6.0 3 'train' 'foo'][3 Timestamp('2018-08-19 00:00:00') 9.0 3 'test' 'foo'][4 Timestamp('2018-08-19 00:00:00') 10.0 3 'train' 'foo']] # 數據總結 print(df2.describe()) A C D count 4.000000 4.000000 4.0 mean 2.500000 6.500000 3.0 std 1.290994 4.041452 0.0 min 1.000000 1.000000 3.0 25% 1.750000 4.750000 3.0 50% 2.500000 7.500000 3.0 75% 3.250000 9.250000 3.0 max 4.000000 10.000000 3.0 # 翻轉數據 print(df2.T) # print(np.transpose(df2))等價于上述操作 0 1 2 \ A 1 2 3 B 2018-08-19 00:00:00 2018-08-19 00:00:00 2018-08-19 00:00:00 C 1 6 9 D 3 3 3 E test train test F foo foo foo3 A 4 B 2018-08-19 00:00:00 C 10 D 3 E train F foo ''' axis=1表示行 axis=0表示列默認ascending(升序)為True ascending=True表示升序,ascending=False表示降序下面兩行分別表示按行升序與按行降序 ''' print(df2.sort_index(axis=1,ascending=True)) A B C D E F 0 1 2018-08-19 1.0 3 test foo 1 2 2018-08-19 6.0 3 train foo 2 3 2018-08-19 9.0 3 test foo 3 4 2018-08-19 10.0 3 train foo print(df2.sort_index(axis=1,ascending=False)) F E D C B A 0 foo test 3 1.0 2018-08-19 1 1 foo train 3 6.0 2018-08-19 2 2 foo test 3 9.0 2018-08-19 3 3 foo train 3 10.0 2018-08-19 4 # 表示按列降序與按列升序 print(df2.sort_index(axis=0,ascending=False)) A B C D E F 3 4 2018-08-19 10.0 3 train foo 2 3 2018-08-19 9.0 3 test foo 1 2 2018-08-19 6.0 3 train foo 0 1 2018-08-19 1.0 3 test foo print(df2.sort_index(axis=0,ascending=True)) A B C D E F 0 1 2018-08-19 1.0 3 test foo 1 2 2018-08-19 6.0 3 train foo 2 3 2018-08-19 9.0 3 test foo 3 4 2018-08-19 10.0 3 train foo # 對特定列數值排列 # 表示對C列降序排列 print(df2.sort_values(by='C',ascending=False)) A B C D E F 3 4 2018-08-19 10.0 3 train foo 2 3 2018-08-19 9.0 3 test foo 1 2 2018-08-19 6.0 3 train foo 0 1 2018-08-19 1.0 3 test foo

3.pandas 選擇數據

3.1 實戰篩選

import pandas as pd import numpy as np dates = pd.date_range('20180819', periods=6) df = pd.DataFrame(np.arange(24).reshape((6,4)),index=dates, columns=['A','B','C','D']) print(df) A B C D 2018-08-19 0 1 2 3 2018-08-20 4 5 6 7 2018-08-21 8 9 10 11 2018-08-22 12 13 14 15 2018-08-23 16 17 18 19 2018-08-24 20 21 22 23 # 檢索A列 print(df['A']) 2018-08-19 0 2018-08-20 4 2018-08-21 8 2018-08-22 12 2018-08-23 16 2018-08-24 20 Freq: D, Name: A, dtype: int32 print(df.A) 2018-08-19 0 2018-08-20 4 2018-08-21 8 2018-08-22 12 2018-08-23 16 2018-08-24 20 Freq: D, Name: A, dtype: int32 # 選擇跨越多行或多列 # 選取前3行 print(df[0:3]) A B C D 2018-08-19 0 1 2 3 2018-08-20 4 5 6 7 2018-08-21 8 9 10 11 print(df['2018-08-19':'2018-08-21']) A B C D 2018-08-19 0 1 2 3 2018-08-20 4 5 6 7 2018-08-21 8 9 10 11 # 根據標簽選擇數據 # 獲取特定行或列 # 指定行數據 print(df.loc['20180819']) A 0 B 1 C 2 D 3 Name: 2018-08-19 00:00:00, dtype: int32 # 指定列 # 兩種方式 print(df.loc[:,'A':'C']) A B C 2018-08-19 0 1 2 2018-08-20 4 5 6 2018-08-21 8 9 10 2018-08-22 12 13 14 2018-08-23 16 17 18 2018-08-24 20 21 22 print(df.loc[:,['A','C']]) A C 2018-08-19 0 2 2018-08-20 4 6 2018-08-21 8 10 2018-08-22 12 14 2018-08-23 16 18 2018-08-24 20 22 # 行與列同時檢索 print(df.loc['20180819',['A','B']]) A 0 B 1 Name: 2018-08-19 00:00:00, dtype: int32 # 根據序列iloc # 獲取特定位置的值 print(df.iloc[3,1]) 13 print(df.iloc[3:5,1:3]) # 不包含末尾5或3，同列表切片 B C 2018-08-22 13 14 2018-08-23 17 18 # 跨行操作 print(df.iloc[[1,3,5],1:3]) B C 2018-08-20 5 6 2018-08-22 13 14 2018-08-24 21 22 # 混合選擇 print(df.ix[:3,['A','C']]) A C 2018-08-19 0 2 2018-08-20 4 6 2018-08-21 8 10 print(df.iloc[:3,[0,2]]) # 結果同上 A C 2018-08-19 0 2 2018-08-20 4 6 2018-08-21 8 10 # 通過判斷的篩選 print(df[df.A>8]) A B C D 2018-08-22 12 13 14 15 2018-08-23 16 17 18 19 2018-08-24 20 21 22 23 # 通過判斷的篩選 print(df.loc[df.A>8]) A B C D 2018-08-22 12 13 14 15 2018-08-23 16 17 18 19 2018-08-24 20 21 22 23

3.2 篩選總結
1.iloc 與 ix 區別
★
總結:相同點：iloc 可以取相應的值，操作方便,與 ix 操作類似。
”★
不同點：ix 可以混合選擇，可以填入 column 對應的字符選擇，而 iloc 只能采用 index 索引，對于列數較多情況下，ix 要方便操作許多。
”
2.loc 與 iloc 區別
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總結：相同點：都可以索引處塊數據
”★
不同點：iloc 可以檢索對應值,兩者操作不同。
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3.ix 與 loc、iloc 三者的區別
★
n 總結：ix 是混合 loc 與 iloc 操作
”

如下:對比三者操作,輸出結果相同

print(df.loc['20180819','A':'B']) print(df.iloc[0,0:2]) print(df.ix[0,'A':'B']) A 0 B 1 Name: 2018-08-19 00:00:00, dtype: int32 A 0 B 1 Name: 2018-08-19 00:00:00, dtype: int32 A 0 B 1 Name: 2018-08-19 00:00:00, dtype: int32

4.Pandas 設置值

4.1 創建數據

import pandas as pd import numpy as np # 創建數據 dates = pd.date_range('20180820',periods=6) df = pd.DataFrame(np.arange(24).reshape(6,4), index=dates, columns=['A','B','C','D']) print(df) A B C D 2018-08-20 0 1 2 3 2018-08-21 4 5 6 7 2018-08-22 8 9 10 11 2018-08-23 12 13 14 15 2018-08-24 16 17 18 19 2018-08-25 20 21 22 23

4.2 根據位置設置 loc 和 iloc

# 根據位置設置loc和iloc df.iloc[2,2] = 111 df.loc['20180820','B'] = 2222 print(df) A B C D 2018-08-20 0 2222 2 3 2018-08-21 4 5 6 7 2018-08-22 8 9 111 11 2018-08-23 12 13 14 15 2018-08-24 16 17 18 19 2018-08-25 20 21 22 23

4.3 根據條件設置

# 根據條件設置 # 更改B中的數，而更改的位置取決于4的位置，并設相應位置的數為0 df.B[df.A>4] = 0 print(df) A B C D 2018-08-20 0 2222 2 3 2018-08-21 4 5 6 7 2018-08-22 8 0 111 11 2018-08-23 12 0 14 15 2018-08-24 16 0 18 19 2018-08-25 20 0 22 23 df.B.loc[df.A>4] = 0 print(df) A B C D 2018-08-20 0 2222 2 3 2018-08-21 4 5 6 7 2018-08-22 8 0 111 11 2018-08-23 12 0 14 15 2018-08-24 16 0 18 19 2018-08-25 20 0 22 23

4.4 按行或列設置

# 按行或列設置 # 列批處理，F列全改為NaN df['F'] = np.nan print(df) A B C D F 2018-08-20 0 2222 2 3 NaN 2018-08-21 4 5 6 7 NaN 2018-08-22 8 0 111 11 NaN 2018-08-23 12 0 14 15 NaN 2018-08-24 16 0 18 19 NaN 2018-08-25 20 0 22 23 NaN

4.5 添加 Series 序列(長度必須對齊)

df['E'] = pd.Series([1,2,3,4,5,6], index=pd.date_range('20180820',periods=6)) print(df) A B C D F E 2018-08-20 0 2222 2 3 NaN 1 2018-08-21 4 5 6 7 NaN 2 2018-08-22 8 0 111 11 NaN 3 2018-08-23 12 0 14 15 NaN 4 2018-08-24 16 0 18 19 NaN 5 2018-08-25 20 0 22 23 NaN 6

4.6 設定某行某列為特定值

# 設定某行某列為特定值 df.ix['20180820','A'] = 56 print(df) #ix 以后要剝離了，盡量不要用了 A B C D F E 2018-08-20 56 2222 2 3 NaN 1 2018-08-21 4 5 6 7 NaN 2 2018-08-22 8 0 111 11 NaN 3 2018-08-23 12 0 14 15 NaN 4 2018-08-24 16 0 18 19 NaN 5 2018-08-25 20 0 22 23 NaN 6 df.loc['20180820','A'] = 67 print(df) A B C D F E 2018-08-20 67 2222 2 3 NaN 1 2018-08-21 4 5 6 7 NaN 2 2018-08-22 8 0 111 11 NaN 3 2018-08-23 12 0 14 15 NaN 4 2018-08-24 16 0 18 19 NaN 5 2018-08-25 20 0 22 23 NaN 6 df.iloc[0,0] = 76 print(df) A B C D F E 2018-08-20 76 2222 2 3 NaN 1 2018-08-21 4 5 6 7 NaN 2 2018-08-22 8 0 111 11 NaN 3 2018-08-23 12 0 14 15 NaN 4 2018-08-24 16 0 18 19 NaN 5 2018-08-25 20 0 22 23 NaN 6

4.7 修改一整行數據

# 修改一整行數據 df.iloc[1] = np.nan # df.iloc[1,:]=np.nan print(df) A B C D F E 2018-08-20 76.0 2222.0 2.0 3.0 NaN 1.0 2018-08-21 NaN NaN NaN NaN NaN NaN 2018-08-22 8.0 0.0 111.0 11.0 NaN 3.0 2018-08-23 12.0 0.0 14.0 15.0 NaN 4.0 2018-08-24 16.0 0.0 18.0 19.0 NaN 5.0 2018-08-25 20.0 0.0 22.0 23.0 NaN 6.0 df.loc['20180820'] = np.nan # df.loc['20180820,:']=np.nan print(df) A B C D F E 2018-08-20 NaN NaN NaN NaN NaN NaN 2018-08-21 NaN NaN NaN NaN NaN NaN 2018-08-22 8.0 0.0 111.0 11.0 NaN 3.0 2018-08-23 12.0 0.0 14.0 15.0 NaN 4.0 2018-08-24 16.0 0.0 18.0 19.0 NaN 5.0 2018-08-25 20.0 0.0 22.0 23.0 NaN 6.0 df.ix[2] = np.nan # df.ix[2,:]=np.nan print(df) A B C D F E 2018-08-20 NaN NaN NaN NaN NaN NaN 2018-08-21 NaN NaN NaN NaN NaN NaN 2018-08-22 NaN NaN NaN NaN NaN NaN 2018-08-23 12.0 0.0 14.0 15.0 NaN 4.0 2018-08-24 16.0 0.0 18.0 19.0 NaN 5.0 2018-08-25 20.0 0.0 22.0 23.0 NaN 6.0 df.ix['20180823'] = np.nan print(df) A B C D F E 2018-08-20 NaN NaN NaN NaN NaN NaN 2018-08-21 NaN NaN NaN NaN NaN NaN 2018-08-22 NaN NaN NaN NaN NaN NaN 2018-08-23 NaN NaN NaN NaN NaN NaN 2018-08-24 16.0 0.0 18.0 19.0 NaN 5.0 2018-08-25 20.0 0.0 22.0 23.0 NaN 6.0

5.Pandas 處理丟失數據

5.1 創建含 NaN 的矩陣

# Pandas處理丟失數據 import pandas as pd import numpy as np # 創建含NaN的矩陣 # 如何填充和刪除NaN數據? dates = pd.date_range('20180820',periods=6) df = pd.DataFrame(np.arange(24).reshape((6,4)),index=dates,columns=['A','B','C','D']) print(df) A B C D 2018-08-20 0 1 2 3 2018-08-21 4 5 6 7 2018-08-22 8 9 10 11 2018-08-23 12 13 14 15 2018-08-24 16 17 18 19 2018-08-25 20 21 22 23 # a.reshape(6,4)等價于a.reshape((6,4)) df.iloc[0,1] = np.nan df.iloc[1,2] = np.nan print(df) A B C D 2018-08-20 0 NaN 2.0 3 2018-08-21 4 5.0 NaN 7 2018-08-22 8 9.0 10.0 11 2018-08-23 12 13.0 14.0 15 2018-08-24 16 17.0 18.0 19 2018-08-25 20 21.0 22.0 23

5.2 刪除掉有 NaN 的行或列

# 刪除掉有NaN的行或列 print(df.dropna()) # 默認是刪除掉含有NaN的行 A B C D 2018-08-22 8 9.0 10.0 11 2018-08-23 12 13.0 14.0 15 2018-08-24 16 17.0 18.0 19 2018-08-25 20 21.0 22.0 23 print(df.dropna(axis=0, # 0對行進行操作;1對列進行操作how='any' # 'any':只要存在NaN就drop掉；'all':必須全部是NaN才drop )) A B C D 2018-08-22 8 9.0 10.0 11 2018-08-23 12 13.0 14.0 15 2018-08-24 16 17.0 18.0 19 2018-08-25 20 21.0 22.0 23 # 刪除掉所有含有NaN的列 print(df.dropna(axis=1,how='any' )) A D 2018-08-20 0 3 2018-08-21 4 7 2018-08-22 8 11 2018-08-23 12 15 2018-08-24 16 19 2018-08-25 20 23

5.3 替換 NaN 值為 0 或者其他

# 替換NaN值為0或者其他 print(df.fillna(value=0)) A B C D 2018-08-20 0 0.0 2.0 3 2018-08-21 4 5.0 0.0 7 2018-08-22 8 9.0 10.0 11 2018-08-23 12 13.0 14.0 15 2018-08-24 16 17.0 18.0 19 2018-08-25 20 21.0 22.0 23

5.4 是否有缺失數據 NaN

# 是否有缺失數據NaN # 是否為空 print(df.isnull()) A B C D 2018-08-20 False True False False 2018-08-21 False False True False 2018-08-22 False False False False 2018-08-23 False False False False 2018-08-24 False False False False 2018-08-25 False False False False # 是否為NaN print(df.isna()) A B C D 2018-08-20 False True False False 2018-08-21 False False True False 2018-08-22 False False False False 2018-08-23 False False False False 2018-08-24 False False False False 2018-08-25 False False False False # 檢測某列是否有缺失數據NaN print(df.isnull().any()) A False B True C True D False dtype: bool # 檢測數據中是否存在NaN,如果存在就返回True print(np.any(df.isnull())==True) True

6.Pandas 導入導出

6.1 導入數據

import pandas as pd # 加載模塊 # 讀取csv data = pd.read_csv('student.csv') # 打印出data print(data) Student ID name age gender 0 1100 Kelly 22 Female 1 1101 Clo 21 Female 2 1102 Tilly 22 Female 3 1103 Tony 24 Male 4 1104 David 20 Male 5 1105 Catty 22 Female 6 1106 M 3 Female 7 1107 N 43 Male 8 1108 A 13 Male 9 1109 S 12 Male 10 1110 David 33 Male 11 1111 Dw 3 Female 12 1112 Q 23 Male 13 1113 W 21 Female # 前三行 print(data.head(3)) Student ID name age gender 0 1100 Kelly 22 Female 1 1101 Clo 21 Female 2 1102 Tilly 22 Female # 后三行 print(data.tail(3)) Student ID name age gender 11 1111 Dw 3 Female 12 1112 Q 23 Male 13 1113 W 21 Female

6.2 導出數據

# 將資料存取成pickle data.to_pickle('student.pickle') # 讀取pickle文件并打印 print(pd.read_pickle('student.pickle')) Student ID name age gender 0 1100 Kelly 22 Female 1 1101 Clo 21 Female 2 1102 Tilly 22 Female 3 1103 Tony 24 Male 4 1104 David 20 Male 5 1105 Catty 22 Female 6 1106 M 3 Female 7 1107 N 43 Male 8 1108 A 13 Male 9 1109 S 12 Male 10 1110 David 33 Male 11 1111 Dw 3 Female 12 1112 Q 23 Male 13 1113 W 21 Female

7.Pandas 合并操作

7.1 Pandas 合并 concat

import pandas as pd import numpy as np# 定義資料集 df1 = pd.DataFrame(np.ones((3,4))*0, columns=['a','b','c','d']) df2 = pd.DataFrame(np.ones((3,4))*1, columns=['a','b','c','d']) df3 = pd.DataFrame(np.ones((3,4))*2, columns=['a','b','c','d']) print(df1) a b c d 0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 print(df2) a b c d 0 1.0 1.0 1.0 1.0 1 1.0 1.0 1.0 1.0 2 1.0 1.0 1.0 1.0 print(df3) a b c d 0 2.0 2.0 2.0 2.0 1 2.0 2.0 2.0 2.0 2 2.0 2.0 2.0 2.0 # concat縱向合并 res = pd.concat([df1,df2,df3],axis=0)# 打印結果 print(res) a b c d 0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 0 1.0 1.0 1.0 1.0 1 1.0 1.0 1.0 1.0 2 1.0 1.0 1.0 1.0 0 2.0 2.0 2.0 2.0 1 2.0 2.0 2.0 2.0 2 2.0 2.0 2.0 2.0 # 上述合并過程中，index重復，下面給出重置index方法 # 只需要將index_ignore設定為True即可 res = pd.concat([df1,df2,df3],axis=0,ignore_index=True)# 打印結果 print(res) a b c d 0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 3 1.0 1.0 1.0 1.0 4 1.0 1.0 1.0 1.0 5 1.0 1.0 1.0 1.0 6 2.0 2.0 2.0 2.0 7 2.0 2.0 2.0 2.0 8 2.0 2.0 2.0 2.0 # join 合并方式 #定義資料集 df1 = pd.DataFrame(np.ones((3,4))*0, columns=['a','b','c','d'], index=[1,2,3]) df2 = pd.DataFrame(np.ones((3,4))*1, columns=['b','c','d','e'], index=[2,3,4]) print(df1) a b c d 1 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 3 0.0 0.0 0.0 0.0 print(df2) b c d e 2 1.0 1.0 1.0 1.0 3 1.0 1.0 1.0 1.0 4 1.0 1.0 1.0 1.0 ''' join='outer',函數默認為join='outer'。此方法是依照column來做縱向合并，有相同的column上下合并在一起，其他獨自的column各自成列，原來沒有值的位置皆為NaN填充。 ''' # 縱向"外"合并df1與df2 res = pd.concat([df1,df2],axis=0,join='outer')print(res) a b c d e 1 0.0 0.0 0.0 0.0 NaN 2 0.0 0.0 0.0 0.0 NaN 3 0.0 0.0 0.0 0.0 NaN 2 NaN 1.0 1.0 1.0 1.0 3 NaN 1.0 1.0 1.0 1.0 4 NaN 1.0 1.0 1.0 1.0 # 修改index res = pd.concat([df1,df2],axis=0,join='outer',ignore_index=True)print(res) a b c d e 0 0.0 0.0 0.0 0.0 NaN 1 0.0 0.0 0.0 0.0 NaN 2 0.0 0.0 0.0 0.0 NaN 3 NaN 1.0 1.0 1.0 1.0 4 NaN 1.0 1.0 1.0 1.0 5 NaN 1.0 1.0 1.0 1.0 # join='inner'合并相同的字段 # 縱向"內"合并df1與df2 res = pd.concat([df1,df2],axis=0,join='inner') # 打印結果 print(res) b c d 1 0.0 0.0 0.0 2 0.0 0.0 0.0 3 0.0 0.0 0.0 2 1.0 1.0 1.0 3 1.0 1.0 1.0 4 1.0 1.0 1.0 # join_axes(依照axes合并) #定義資料集 df1 = pd.DataFrame(np.ones((3,4))*0, columns=['a','b','c','d'], index=[1,2,3]) df2 = pd.DataFrame(np.ones((3,4))*1, columns=['b','c','d','e'], index=[2,3,4]) print(df1) a b c d 1 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 3 0.0 0.0 0.0 0.0 print(df2) b c d e 2 1.0 1.0 1.0 1.0 3 1.0 1.0 1.0 1.0 4 1.0 1.0 1.0 1.0 # 依照df1.index進行橫向合并 res = pd.concat([df1,df2],axis=1,join_axes=[df1.index]) print(res) a b c d b c d e 1 0.0 0.0 0.0 0.0 NaN NaN NaN NaN 2 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 3 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 # 移除join_axes參數,打印結果 res = pd.concat([df1,df2],axis=1) print(res) a b c d b c d e 1 0.0 0.0 0.0 0.0 NaN NaN NaN NaN 2 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 3 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 4 NaN NaN NaN NaN 1.0 1.0 1.0 1.0 # append(添加數據) # append只有縱向合并，沒有橫向合并 #定義資料集 df1 = pd.DataFrame(np.ones((3,4))*0, columns=['a','b','c','d']) df2 = pd.DataFrame(np.ones((3,4))*1, columns=['a','b','c','d']) df3 = pd.DataFrame(np.ones((3,4))*2, columns=['a','b','c','d']) s1 = pd.Series([1,2,3,4], index=['a','b','c','d']) # 將df2合并到df1下面,以及重置index,并打印出結果 res = df1.append(df2,ignore_index=True) print(res) a b c d 0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 3 1.0 1.0 1.0 1.0 4 1.0 1.0 1.0 1.0 5 1.0 1.0 1.0 1.0 # 合并多個df,將df2與df3合并至df1的下面,以及重置index,并打印出結果 res = df1.append([df2,df3], ignore_index=True) print(res) a b c d 0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 3 1.0 1.0 1.0 1.0 4 1.0 1.0 1.0 1.0 5 1.0 1.0 1.0 1.0 6 2.0 2.0 2.0 2.0 7 2.0 2.0 2.0 2.0 8 2.0 2.0 2.0 2.0 # 合并series,將s1合并至df1，以及重置index，并打印結果 res = df1.append(s1,ignore_index=True) print(res) a b c d 0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 3 1.0 2.0 3.0 4.0 # 總結:兩種常用合并方式 res = pd.concat([df1, df2, df3], axis=0, ignore_index=True) res1 = df1.append([df2, df3], ignore_index=True) print(res) print(res1) a b c d 0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 3 1.0 1.0 1.0 1.0 4 1.0 1.0 1.0 1.0 5 1.0 1.0 1.0 1.0 6 2.0 2.0 2.0 2.0 7 2.0 2.0 2.0 2.0 8 2.0 2.0 2.0 2.0a b c d 0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 3 1.0 1.0 1.0 1.0 4 1.0 1.0 1.0 1.0 5 1.0 1.0 1.0 1.0 6 2.0 2.0 2.0 2.0 7 2.0 2.0 2.0 2.0 8 2.0 2.0 2.0 2.0

7.2.Pandas 合并 merge

7.2.1 定義資料集并打印出

import pandas as pd # 依據一組key合并 # 定義資料集并打印出 left = pd.DataFrame({'key' : ['K0','K1','K2','K3'],'A' : ['A0','A1','A2','A3'],'B' : ['B0','B1','B2','B3']})right = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],'C' : ['C0', 'C1', 'C2', 'C3'],'D' : ['D0', 'D1', 'D2', 'D3']}) print(left) A B key 0 A0 B0 K0 1 A1 B1 K1 2 A2 B2 K2 3 A3 B3 K3 print(right) C D key 0 C0 D0 K0 1 C1 D1 K1 2 C2 D2 K2 3 C3 D3 K3

7.2.2 依據 key column 合并,并打印

# 依據key column合并,并打印 res = pd.merge(left,right,on='key') print(res) A B key C D 0 A0 B0 K0 C0 D0 1 A1 B1 K1 C1 D1 2 A2 B2 K2 C2 D2 3 A3 B3 K3 C3 D3 # 依據兩組key合并 #定義資料集并打印出 left = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],'key2': ['K0', 'K1', 'K0', 'K1'],'A': ['A0', 'A1', 'A2', 'A3'],'B': ['B0', 'B1', 'B2', 'B3']}) right = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],'key2': ['K0', 'K0', 'K0', 'K0'],'C': ['C0', 'C1', 'C2', 'C3'],'D': ['D0', 'D1', 'D2', 'D3']}) print(left) A B key1 key2 0 A0 B0 K0 K0 1 A1 B1 K0 K1 2 A2 B2 K1 K0 3 A3 B3 K2 K1 print(right) C D key1 key2 0 C0 D0 K0 K0 1 C1 D1 K1 K0 2 C2 D2 K1 K0 3 C3 D3 K2 K0

7.2.3 兩列合并

# 依據key1與key2 columns進行合并，并打印出四種結果['left', 'right', 'outer', 'inner'] res = pd.merge(left, right, on=['key1', 'key2'], how='inner') print(res) A B key1 key2 C D 0 A0 B0 K0 K0 C0 D0 1 A2 B2 K1 K0 C1 D1 2 A2 B2 K1 K0 C2 D2 res = pd.merge(left, right, on=['key1', 'key2'], how='outer') print(res) A B key1 key2 C D 0 A0 B0 K0 K0 C0 D0 1 A1 B1 K0 K1 NaN NaN 2 A2 B2 K1 K0 C1 D1 3 A2 B2 K1 K0 C2 D2 4 A3 B3 K2 K1 NaN NaN 5 NaN NaN K2 K0 C3 D3 res = pd.merge(left, right, on=['key1', 'key2'], how='left') print(res) A B key1 key2 C D 0 A0 B0 K0 K0 C0 D0 1 A1 B1 K0 K1 NaN NaN 2 A2 B2 K1 K0 C1 D1 3 A2 B2 K1 K0 C2 D2 4 A3 B3 K2 K1 NaN NaN res = pd.merge(left, right, on=['key1', 'key2'], how='right') print(res) A B key1 key2 C D 0 A0 B0 K0 K0 C0 D0 1 A2 B2 K1 K0 C1 D1 2 A2 B2 K1 K0 C2 D2 3 NaN NaN K2 K0 C3 D3

7.2.4 Indicator 設置合并列名稱

# Indicator df1 = pd.DataFrame({'col1':[0,1],'col_left':['a','b']}) df2 = pd.DataFrame({'col1':[1,2,2],'col_right':[2,2,2]}) print(df1) col1 col_left 0 0 a 1 1 b print(df2) col1 col_right 0 1 2 1 2 2 2 2 2 # 依據col1進行合并,并啟用indicator=True,最后打印 res = pd.merge(df1,df2,on='col1',how='outer',indicator=True) print(res) col1 col_left col_right _merge 0 0 a NaN left_only 1 1 b 2.0 both 2 2 NaN 2.0 right_only 3 2 NaN 2.0 right_only # 自定義indicator column的名稱,并打印出 res = pd.merge(df1,df2,on='col1',how='outer',indicator='indicator_column') print(res) col1 col_left col_right indicator_column 0 0 a NaN left_only 1 1 b 2.0 both 2 2 NaN 2.0 right_only 3 2 NaN 2.0 right_only

7.2.5 依據 index 合并

# 依據index合并 #定義資料集并打印出 left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],'B': ['B0', 'B1', 'B2']},index=['K0', 'K1', 'K2']) right = pd.DataFrame({'C': ['C0', 'C2', 'C3'],'D': ['D0', 'D2', 'D3']},index=['K0', 'K2', 'K3']) print(left) A B K0 A0 B0 K1 A1 B1 K2 A2 B2 print(right) C D K0 C0 D0 K2 C2 D2 K3 C3 D3 # 依據左右資料集的index進行合并,how='outer',并打印 res = pd.merge(left,right,left_index=True,right_index=True,how='outer') print(res) A B C D K0 A0 B0 C0 D0 K1 A1 B1 NaN NaN K2 A2 B2 C2 D2 K3 NaN NaN C3 D3 # 依據左右資料集的index進行合并,how='inner',并打印 res = pd.merge(left,right,left_index=True,right_index=True,how='inner') print(res) A B C D K0 A0 B0 C0 D0 K2 A2 B2 C2 D2

7.2.6 解決 overlapping 的問題

# 解決overlapping的問題 #定義資料集 boys = pd.DataFrame({'k': ['K0', 'K1', 'K2'], 'age': [1, 2, 3]}) girls = pd.DataFrame({'k': ['K0', 'K0', 'K3'], 'age': [4, 5, 6]}) print(boys) age k 0 1 K0 1 2 K1 2 3 K2 print(girls) age k 0 4 K0 1 5 K0 2 6 K3 # 使用suffixes解決overlapping的問題 # 比如將上面兩個合并時,age重復了,則可通過suffixes設置,以此保證不重復,不同名 res = pd.merge(boys,girls,on='k',suffixes=['_boy','_girl'],how='inner') print(res) age_boy k age_girl 0 1 K0 4 1 1 K0 5

8.Pandas plot 出圖

import pandas as pd import numpy as np import matplotlib.pyplot as pltdata = pd.Series(np.random.randn(1000), index=np.arange(1000)) print(data) 0 0.143408 1 -1.936116 2 -1.488609 中間數據略 998 -1.617468 999 -0.115447 Length: 1000, dtype: float64 print(data.cumsum()) 0 0.143408 1 -1.792708 2 -3.281317 中間數據略 997 19.760929 998 18.143460 999 18.028013 Length: 1000, dtype: float64 # data本來就是一個數據，所以我們可以直接plot data.plot() plt.show() # np.random.randn(1000,4) 隨機生成1000行4列數據 # list("ABCD")會變為['A','B','C','D'] data = pd.DataFrame(np.random.randn(1000,4),index=np.arange(1000),columns=list("ABCD") ) data.cumsum() data.plot() plt.show() ax = data.plot.scatter(x='A',y='B',color='DarkBlue',label='Class1') # 將之下這個 data 畫在上一個 ax 上面 data.plot.scatter(x='A',y='C',color='LightGreen',label='Class2',ax=ax) plt.show()

9.學習來源

光城
https://morvanzhou.github.io/tutorials/data-manipulation/np-pd/

備注：本文代碼可以在github下載

https://github.com/fengdu78/Data-Science-Notes/tree/master/3.pandas

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