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Pandas学习笔记（包括示例代码、运算结果及详细注释）

發(fā)布時間：2024/3/24 编程问答 23 豆豆

生活随笔收集整理的這篇文章主要介紹了 Pandas学习笔记（包括示例代码、运算结果及详细注释）小編覺得挺不錯的,現(xiàn)在分享給大家,幫大家做個參考.

1.Series
2.DataFrame的簡單運用
3.pandas選擇數(shù)據(jù)
- 3.1 實戰(zhàn)篩選
- 3.2 篩選總結(jié)
4.Pandas設(shè)置值
- 4.1 創(chuàng)建數(shù)據(jù)
- 4.2 根據(jù)位置設(shè)置loc和iloc
- 4.3 根據(jù)條件設(shè)置
- 4.4 按行或列設(shè)置
- 4.5 添加Series序列(長度必須對齊)
- 4.6 設(shè)定某行某列為特定值
- 4.7 修改一整行數(shù)據(jù)
5.Pandas處理丟失數(shù)據(jù)
- 5.1 創(chuàng)建含NaN的矩陣
- 5.2 刪除有NaN的行或列
- 5.3 替換NaN值為0或者其他
- 5.4 是否有缺失數(shù)據(jù)NaN
6.Pandas導入導出
- 6.1 導入數(shù)據(jù)
- 6.2 導出數(shù)據(jù)
7.Pandas合并操作
- 7.1 Pandas合并
- - 7.1.1 concat
  - 7.1.2 append添加數(shù)據(jù)
  - 7.1.3 兩種常用合并方式總結(jié)
- 7.2.Pandas 合并 merge
- - 7.2.1 定義資料集并打印
  - 7.2.2 依據(jù)key column合并
  - 7.2.3 兩列合并
  - 7.2.4 Indicator設(shè)置合并列名稱
  - 7.2.5 依據(jù)index合并
  - 7.2.6 解決overlapping的問題
8.Pandas plot出圖

Pandas 是基于NumPy 的一種工具，該工具是為了解決數(shù)據(jù)分析任務(wù)而創(chuàng)建的。Pandas 納入了大量庫和一些標準的數(shù)據(jù)模型，提供了高效地操作大型數(shù)據(jù)集所需的工具。Pandas提供了大量能使我們快速便捷地處理數(shù)據(jù)的函數(shù)和方法。它也是使Python成為強大而高效的數(shù)據(jù)分析環(huán)境的重要因素之一。
若對Numpy不夠了解，可以參考我的另一篇博客：https://blog.csdn.net/strivequeen/article/details/112984264

1.Series

import pandas as pd import numpy as np 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(shè)置，則修改index參數(shù),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的簡單運用

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。

# 指定行標簽和列標簽的數(shù)據(jù) dates = pd.date_range('2021-01-31',periods=6) # dates = pd.date_range('2021-01-31','2021-02-05') # 起始、結(jié)束與上述等價 ''' numpy.random.randn(d0, d1, …, dn)是從標準正態(tài)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(shù)據(jù) ''' df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=['a','b','c','d'])print(df)a b c d 2021-01-31 -0.089777 -0.004411 1.032583 2.570713 2021-02-01 -0.918877 0.600092 -0.711374 -0.087149 2021-02-02 0.000248 0.956044 -0.256532 -2.224948 2021-02-03 1.537203 -1.313810 1.283764 -0.412839 2021-02-04 -0.010970 -0.789566 -0.307353 -0.498532 2021-02-05 0.324443 0.201022 0.956131 -1.140553print(df['b']) 2021-01-31 -0.004411 2021-02-01 0.600092 2021-02-02 0.956044 2021-02-03 -1.313810 2021-02-04 -0.789566 2021-02-05 0.201022 Freq: D, Name: b, dtype: float64 # 未指定行標簽和列標簽的數(shù)據(jù) 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('20210131'),'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 2021-01-31 1.0 3 test foo 1 2 2021-01-31 6.0 3 train foo 2 3 2021-01-31 9.0 3 test foo 3 4 2021-01-31 10.0 3 train fooprint(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('2021-01-31 00:00:00') 1.0 3 'test' 'foo'][2 Timestamp('2021-01-31 00:00:00') 6.0 3 'train' 'foo'][3 Timestamp('2021-01-31 00:00:00') 9.0 3 'test' 'foo'][4 Timestamp('2021-01-31 00:00:00') 10.0 3 'train' 'foo']]# 數(shù)據(jù)總結(jié) print(df2.describe()) # 只對數(shù)字數(shù)據(jù)進行描述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(zhuǎn)數(shù)據(jù) print(df2.T) # print(np.transpose(df2))為等價操作0 1 2 \ A 1 2 3 B 2021-01-31 00:00:00 2021-01-31 00:00:00 2021-01-31 00:00:00 C 1 6 9 D 3 3 3 E test train test F foo foo foo 3 A 4 B 2021-01-31 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 ''' print(df2.sort_index(axis=1,ascending=True))A B C D E F 0 1 2021-01-31 1.0 3 test foo 1 2 2021-01-31 6.0 3 train foo 2 3 2021-01-31 9.0 3 test foo 3 4 2021-01-31 10.0 3 train fooprint(df2.sort_index(axis=1,ascending=False))F E D C B A 0 foo test 3 1.0 2021-01-31 1 1 foo train 3 6.0 2021-01-31 2 2 foo test 3 9.0 2021-01-31 3 3 foo train 3 10.0 2021-01-31 4print(df2.sort_index(axis=0,ascending=False)) # 表示按列降序與按列升序A B C D E F 3 4 2021-01-31 10.0 3 train foo 2 3 2021-01-31 9.0 3 test foo 1 2 2021-01-31 6.0 3 train foo 0 1 2021-01-31 1.0 3 test fooprint(df2.sort_index(axis=0,ascending=True))A B C D E F 0 1 2021-01-31 1.0 3 test foo 1 2 2021-01-31 6.0 3 train foo 2 3 2021-01-31 9.0 3 test foo 3 4 2021-01-31 10.0 3 train foo# 對特定列數(shù)值排列 print(df2.sort_values(by='C',ascending=False)) # 表示對C列降序排列A B C D E F 3 4 2021-01-31 10.0 3 train foo 2 3 2021-01-31 9.0 3 test foo 1 2 2021-01-31 6.0 3 train foo 0 1 2021-01-31 1.0 3 test foo

3.pandas選擇數(shù)據(jù)

3.1 實戰(zhàn)篩選

dates = pd.date_range('20210131', periods=6) df = pd.DataFrame(np.arange(24).reshape((6,4)),index=dates, columns=['A','B','C','D']) print(df)A B C D 2021-01-31 0 1 2 3 2021-02-01 4 5 6 7 2021-02-02 8 9 10 11 2021-02-03 12 13 14 15 2021-02-04 16 17 18 19 2021-02-05 20 21 22 23print(df.A) # 與 print(df['A']) 等價 2021-01-31 0 2021-02-01 4 2021-02-02 8 2021-02-03 12 2021-02-04 16 2021-02-05 20 Freq: D, Name: A, dtype: int32# 選擇跨越多行或多列 print(df[0:3]) # 選取前3行，與 print(df['2021-01-31':'2021-02-02']) 等價A B C D 2021-01-31 0 1 2 3 2021-02-01 4 5 6 7 2021-02-02 8 9 10 11# 根據(jù)標簽選擇數(shù)據(jù)，獲取特定行或列 print(df.loc['20210202']) # 指定行數(shù)據(jù) A 8 B 9 C 10 D 11 Name: 2021-02-02 00:00:00, dtype: int32# 指定列 print(df.loc[:,'A':'B']) # 與 print(df.loc[:,['A','B']]) 等價A B 2021-01-31 0 1 2021-02-01 4 5 2021-02-02 8 9 2021-02-03 12 13 2021-02-04 16 17 2021-02-05 20 21# 行與列同時檢索 print(df.loc['20210131',['A','B']]) A 0 B 1 Name: 2021-01-31 00:00:00, dtype: int32# 根據(jù)序列iloc，獲取特定位置的值 print(df.iloc[3,1]) 13print(df.iloc[3:5,1:3]) # 不包含末尾5或3，同列表切片B C 2021-02-03 13 14 2021-02-04 17 18# 跨行操作 print(df.iloc[[1,3,5],1:3])B C 2021-02-01 5 6 2021-02-03 13 14 2021-02-05 21 22print(df.iloc[:3,[0,2]]) # 混合選擇A C 2021-01-31 0 2 2021-02-01 4 6 2021-02-02 8 10# 通過判斷的篩選 print(df[df.A>8]) # 與 print(df.loc[df.A>8]) 等價A B C D 2021-02-03 12 13 14 15 2021-02-04 16 17 18 19 2021-02-05 20 21 22 23

3.2 篩選總結(jié)

iloc與ix
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(shù)較多情況下，ix要方便操作許多。

loc與iloc
相同點：都可以索引處塊數(shù)據(jù)
不同點：iloc可以檢索對應值,兩者操作不同。

3.ix與loc、iloc
ix是混合loc與iloc操作

print(df.loc['20210131','A':'B']) print(df.iloc[0,0:2]) print(df.ix[0,'A':'B']) # 以上三種結(jié)果均為： A 0 B 1 Name: 2021-01-31 00:00:00, dtype: int32

4.Pandas設(shè)置值

4.1 創(chuàng)建數(shù)據(jù)

# 創(chuàng)建數(shù)據(jù) dates = pd.date_range('20210131',periods=6) df = pd.DataFrame(np.arange(24).reshape(6,4), index=dates, columns=['A','B','C','D']) print(df)A B C D 2021-01-31 0 1 2 3 2021-02-01 4 5 6 7 2021-02-02 8 9 10 11 2021-02-03 12 13 14 15 2021-02-04 16 17 18 19 2021-02-05 20 21 22 23

4.2 根據(jù)位置設(shè)置loc和iloc

# 根據(jù)位置設(shè)置loc和iloc df.iloc[2,2] = 111 df.loc['20210131','B'] = 2222 print(df)A B C D 2021-01-31 0 2222 2 3 2021-02-01 4 5 6 7 2021-02-02 8 9 111 11 2021-02-03 12 13 14 15 2021-02-04 16 17 18 19 2021-02-05 20 21 22 23

4.3 根據(jù)條件設(shè)置

# 更改B中的數(shù)，而更改的位置取決于4的位置，并設(shè)相應位置的數(shù)為0 df.B[df.A>4] = 0 # 與 df.B.loc[df.A>4] = 0 等價 print(df)A B C D 2021-01-31 0 2222 2 3 2021-02-01 4 5 6 7 2021-02-02 8 0 111 11 2021-02-03 12 0 14 15 2021-02-04 16 0 18 19 2021-02-05 20 0 22 23

4.4 按行或列設(shè)置

df['F'] = np.nan # 列批處理，F列全改為NaN print(df)A B C D F 2021-01-31 0 2222 2 3 NaN 2021-02-01 4 5 6 7 NaN 2021-02-02 8 0 111 11 NaN 2021-02-03 12 0 14 15 NaN 2021-02-04 16 0 18 19 NaN 2021-02-05 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('20210131',periods=6)) print(df)A B C D F E 2021-01-31 0 2222 2 3 NaN 1 2021-02-01 4 5 6 7 NaN 2 2021-02-02 8 0 111 11 NaN 3 2021-02-03 12 0 14 15 NaN 4 2021-02-04 16 0 18 19 NaN 5 2021-02-05 20 0 22 23 NaN 6

4.6 設(shè)定某行某列為特定值

#ix 以后要剝離了，盡量不要用了df.loc['20210131','A'] = 67 # 與 df.iloc[0,0] = 67 等價 print(df)A B C D F E 2021-01-31 67 2222 2 3 NaN 1 2021-02-01 4 5 6 7 NaN 2 2021-02-02 8 0 111 11 NaN 3 2021-02-03 12 0 14 15 NaN 4 2021-02-04 16 0 18 19 NaN 5 2021-02-05 20 0 22 23 NaN 6

4.7 修改一整行數(shù)據(jù)

df.iloc[1] = np.nan # df.iloc[1,:]=np.nan print(df)A B C D F E 2021-01-31 67.0 2222.0 2.0 3.0 NaN 1.0 2021-02-01 NaN NaN NaN NaN NaN NaN 2021-02-02 8.0 0.0 111.0 11.0 NaN 3.0 2021-02-03 12.0 0.0 14.0 15.0 NaN 4.0 2021-02-04 16.0 0.0 18.0 19.0 NaN 5.0 2021-02-05 20.0 0.0 22.0 23.0 NaN 6.0df.loc['20210131'] = np.nan # df.loc['20210131,:']=np.nan print(df)A B C D F E 2021-01-31 NaN NaN NaN NaN NaN NaN 2021-02-01 NaN NaN NaN NaN NaN NaN 2021-02-02 8.0 0.0 111.0 11.0 NaN 3.0 2021-02-03 12.0 0.0 14.0 15.0 NaN 4.0 2021-02-04 16.0 0.0 18.0 19.0 NaN 5.0 2021-02-05 20.0 0.0 22.0 23.0 NaN 6.0

5.Pandas處理丟失數(shù)據(jù)

5.1 創(chuàng)建含NaN的矩陣

# 創(chuàng)建含NaN的矩陣 dates = pd.date_range('20210131',periods=6) df = pd.DataFrame(np.arange(24).reshape((6,4)),index=dates,columns=['A','B','C','D']) print(df)A B C D 2021-01-31 0 1 2 3 2021-02-01 4 5 6 7 2021-02-02 8 9 10 11 2021-02-03 12 13 14 15 2021-02-04 16 17 18 19 2021-02-05 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 2021-01-31 0 NaN 2.0 3 2021-02-01 4 5.0 NaN 7 2021-02-02 8 9.0 10.0 11 2021-02-03 12 13.0 14.0 15 2021-02-04 16 17.0 18.0 19 2021-02-05 20 21.0 22.0 23

5.2 刪除有NaN的行或列

print(df.dropna()) # 默認是刪除掉含有NaN的行A B C D 2021-02-02 8 9.0 10.0 11 2021-02-03 12 13.0 14.0 15 2021-02-04 16 17.0 18.0 19 2021-02-05 20 21.0 22.0 23print(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 )) # 結(jié)果同上# 刪除掉所有含有NaN的列 print(df.dropna(axis=1,how='any' ))A D 2021-01-31 0 3 2021-02-01 4 7 2021-02-02 8 11 2021-02-03 12 15 2021-02-04 16 19 2021-02-05 20 23

5.3 替換NaN值為0或者其他

print(df.fillna(value=0))A B C D 2021-01-31 0 0.0 2.0 3 2021-02-01 4 5.0 0.0 7 2021-02-02 8 9.0 10.0 11 2021-02-03 12 13.0 14.0 15 2021-02-04 16 17.0 18.0 19 2021-02-05 20 21.0 22.0 23

5.4 是否有缺失數(shù)據(jù)NaN

print(df.isnull()) # 與 print(df.isna()) 等價A B C D 2021-01-31 False True False False 2021-02-01 False False True False 2021-02-02 False False False False 2021-02-03 False False False False 2021-02-04 False False False False 2021-02-05 False False False False# 檢測某列是否有缺失數(shù)據(jù)NaN print(df.isnull().any()) A False B True C True D False dtype: bool# 檢測數(shù)據(jù)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(shù)據(jù)

import pandas as pd # 加載模塊 data = pd.read_csv('student.csv') # 讀取csv print(data) # 打印出dataStudent 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 Femaleprint(data.head(3)) # 前三行Student ID name age gender 0 1100 Kelly 22 Female 1 1101 Clo 21 Female 2 1102 Tilly 22 Femaleprint(data.tail(3)) # 后三行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(shù)據(jù)

# 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'))

7.Pandas合并操作

7.1 Pandas合并

7.1.1 concat

# 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 0.0print(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 1.0print(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) 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，重置index方法：只需要將index_ignore設(shè)定為True即可 res = pd.concat([df1,df2,df3],axis=0,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# 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(shù)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(nèi)"合并df1與df2 res = pd.concat([df1,df2],axis=0,join='inner') 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# 橫向合并 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

7.1.2 append添加數(shù)據(jù)

# 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(jié)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(jié)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(jié)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

7.1.3 兩種常用合并方式總結(jié)

res = pd.concat([df1, df2, df3], axis=0, ignore_index=True) res1 = df1.append([df2, df3], ignore_index=True) # 上述兩種結(jié)果一樣：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

7.2.Pandas 合并 merge

7.2.1 定義資料集并打印

# 依據(jù)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)key A B 0 K0 A0 B0 1 K1 A1 B1 2 K2 A2 B2 3 K3 A3 B3 print(right)key C D 0 K0 C0 D0 1 K1 C1 D1 2 K2 C2 D2 3 K3 C3 D3

7.2.2 依據(jù)key column合并

res = pd.merge(left,right,on='key') print(res)key A B C D 0 K0 A0 B0 C0 D0 1 K1 A1 B1 C1 D1 2 K2 A2 B2 C2 D2 3 K3 A3 B3 C3 D3

7.2.3 兩列合并

# 依據(jù)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)key1 key2 A B 0 K0 K0 A0 B0 1 K0 K1 A1 B1 2 K1 K0 A2 B2 3 K2 K1 A3 B3 print(right)key1 key2 C D 0 K0 K0 C0 D0 1 K1 K0 C1 D1 2 K1 K0 C2 D2 3 K2 K0 C3 D3# 依據(jù)key1與key2 columns進行合并，并打印出四種結(jié)果['left', 'right', 'outer', 'inner'] res = pd.merge(left, right, on=['key1', 'key2'], how='inner') print(res)key1 key2 A B C D 0 K0 K0 A0 B0 C0 D0 1 K1 K0 A2 B2 C1 D1 2 K1 K0 A2 B2 C2 D2res = pd.merge(left, right, on=['key1', 'key2'], how='outer') print(res)key1 key2 A B C D 0 K0 K0 A0 B0 C0 D0 1 K0 K1 A1 B1 NaN NaN 2 K1 K0 A2 B2 C1 D1 3 K1 K0 A2 B2 C2 D2 4 K2 K1 A3 B3 NaN NaN 5 K2 K0 NaN NaN C3 D3res = pd.merge(left, right, on=['key1', 'key2'], how='left') print(res)key1 key2 A B C D 0 K0 K0 A0 B0 C0 D0 1 K0 K1 A1 B1 NaN NaN 2 K1 K0 A2 B2 C1 D1 3 K1 K0 A2 B2 C2 D2 4 K2 K1 A3 B3 NaN NaNres = pd.merge(left, right, on=['key1', 'key2'], how='right') print(res)key1 key2 A B C D 0 K0 K0 A0 B0 C0 D0 1 K1 K0 A2 B2 C1 D1 2 K1 K0 A2 B2 C2 D2 3 K2 K0 NaN NaN C3 D3

7.2.4 Indicator設(shè)置合并列名稱

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(jù)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(jù)index合并

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(jù)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(jù)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

boys = pd.DataFrame({'k': ['K0', 'K1', 'K2'], 'age': [1, 2, 3]}) girls = pd.DataFrame({'k': ['K0', 'K0', 'K3'], 'age': [4, 5, 6]}) print(boys)k age 0 K0 1 1 K1 2 2 K2 3 print(girls)k age 0 K0 4 1 K0 5 2 K3 6# 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(shè)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)k age_boy age_girl 0 K0 1 4 1 K0 1 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 -0.090306 1 0.537896 2 0.577394 3 0.298975 4 -0.506510... 995 0.243735 996 0.462577 997 0.054838 998 -0.514172 999 -0.592451 Length: 1000, dtype: float64print(data.cumsum()) 0 -0.090306 1 0.447590 2 1.024985 3 1.323960 4 0.817450... 995 20.989576 996 21.452154 997 21.506992 998 20.992820 999 20.400369 Length: 1000, dtype: float64# data本來就是一個數(shù)據(jù)，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(shù)據(jù) # list("ABCD")會變?yōu)閇'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()

總結(jié)

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