基本操作進階操作Series 和 Datetime索引數據清洗層次化索引可視化

基本操作

導入 Pandas 庫并簡寫為 pd，并輸出版本號

import?pandas?as?pd pd.__version__ '0.24.2'

2.從列表創建 Series

arr?=?[0,?1,?2,?3,?4] df?=?pd.Series(arr)?#?如果不指定索引，則默認從?0?開始 df 0????0 1????1 2????2 3????3 4????4 dtype:?int64

3.從字典創建 Series

d?=?{'a':1,'b':2,'c':3,'d':4,'e':5} df?=?pd.Series(d) df a????1 b????2 c????3 d????4 e????5 dtype:?int64

4.從 NumPy 數組創建 DataFrame

dates?=?pd.date_range('today',periods=6)?#?定義時間序列作為?index num_arr?=?np.random.randn(6,4)?#?傳入?numpy?隨機數組 columns?=?['A','B','C','D']?#?將列表作為列名 df1?=?pd.DataFrame(num_arr,?index?=?dates,?columns?=?columns) df1
ABCD2020-02-04 05:36:17.2737592020-02-05 05:36:17.2737592020-02-06 05:36:17.2737592020-02-07 05:36:17.2737592020-02-08 05:36:17.2737592020-02-09 05:36:17.273759

0.560268	-0.559426	1.176857	0.885549
2.396094	-0.720063	-1.144393	-0.149686
0.036016	-1.032553	0.526661	0.524164
0.120952	-0.495401	-0.006828	-2.375663
1.125484	0.685709	-0.144614	-0.398538
-0.027859	0.331943	-1.256073	-0.659318

5.從CSV中創建 DataFrame，分隔符為;，編碼格式為gbk

#?df?=?pd.read_csv('test.csv',?encoding='gbk,?sep=';')

6.從字典對象data創建DataFrame，設置索引為labels

import?numpy?as?npdata?=?{'animal':?['cat',?'cat',?'snake',?'dog',?'dog',?'cat',?'snake',?'cat',?'dog',?'dog'],'age':?[2.5,?3,?0.5,?np.nan,?5,?2,?4.5,?np.nan,?7,?3],'visits':?[1,?3,?2,?3,?2,?3,?1,?1,?2,?1],'priority':?['yes',?'yes',?'no',?'yes',?'no',?'no',?'no',?'yes',?'no',?'no']}labels?=?['a',?'b',?'c',?'d',?'e',?'f',?'g',?'h',?'i',?'j'] df?=?pd.DataFrame(data,?index=labels) df
animalagevisitspriorityabcdefghij

cat	2.5	1	yes
cat	3.0	3	yes
snake	0.5	2	no
dog	NaN	3	yes
dog	5.0	2	no
cat	2.0	3	no
snake	4.5	1	no
cat	NaN	1	yes
dog	7.0	2	no
dog	3.0	1	no

7.顯示DataFrame的基礎信息，包括行的數量；列名；每一列值的數量、類型

df.info() #?方法二 #?df.describe() <class?'pandas.core.frame.DataFrame'> Index:?10?entries,?a?to?j Data?columns?(total?4?columns): animal??????10?non-null?object age?????????8?non-null?float64 visits??????10?non-null?int64 priority????10?non-null?object dtypes:?float64(1),?int64(1),?object(2) memory?usage:?400.0+?bytes

8.展示df的前3行

df.iloc[:3] #?方法二 #df.head(3)
animalagevisitspriorityabc

cat	2.5	1	yes
cat	3.0	3	yes
snake	0.5	2	no

9.取出df的animal和age列

df.loc[:,?['animal',?'age']] #?方法二 #?df[['animal',?'age']]
animalageabcdefghij

cat	2.5
cat	3.0
snake	0.5
dog	NaN
dog	5.0
cat	2.0
snake	4.5
cat	NaN
dog	7.0
dog	3.0

10.取出索引為[3, 4, 8]行的animal和age列

df.loc[df.index[[3,?4,?8]],?['animal',?'age']]
animalagedei

dog	NaN
dog	5.0
dog	7.0

11.取出age值大于3的行

df[df['age']?>?3]
animalagevisitspriorityegi

dog	5.0	2	no
snake	4.5	1	no
dog	7.0	2	no

12.取出age值缺失的行

df[df['age'].isnull()]
animalagevisitsprioritydh

dog	NaN	3	yes
cat	NaN	1	yes

13.取出age在2,4間的行（不含）

df[(df['age']>2)?&?(df['age']>4)] #?方法二 #df[df['age'].between(2,?4)]
animalagevisitspriorityegi

dog	5.0	2	no
snake	4.5	1	no
dog	7.0	2	no

14.f行的age改為1.5

df.loc['f',?'age']?=?1.5

15.計算visits的總和

df['visits'].sum() 19

16.計算每個不同種類animal的age的平均數

df.groupby('animal')['age'].mean() animal cat??????2.333333 dog??????5.000000 snake????2.500000 Name:?age,?dtype:?float64

17.計算df中每個種類animal的數量

df['animal'].value_counts() cat??????4 dog??????4 snake????2 Name:?animal,?dtype:?int64

18.先按age降序排列，后按visits升序排列

df.sort_values(by=['age',?'visits'],?ascending=[False,?True])
animalagevisitspriorityiegjbafchd

dog	7.0	2	no
dog	5.0	2	no
snake	4.5	1	no
dog	3.0	1	no
cat	3.0	3	yes
cat	2.5	1	yes
cat	1.5	3	no
snake	0.5	2	no
cat	NaN	1	yes
dog	NaN	3	yes

19.將priority列中的yes, no替換為布爾值True, False

df['priority']?=?df['priority'].map({'yes':?True,?'no':?False}) df
animalagevisitspriorityabcdefghij

cat	2.5	1	True
cat	3.0	3	True
snake	0.5	2	False
dog	NaN	3	True
dog	5.0	2	False
cat	1.5	3	False
snake	4.5	1	False
cat	NaN	1	True
dog	7.0	2	False
dog	3.0	1	False

20.將animal列中的snake替換為python

df['animal']?=?df['animal'].replace('snake',?'python') df
animalagevisitspriorityabcdefghij

cat	2.5	1	True
cat	3.0	3	True
python	0.5	2	False
dog	NaN	3	True
dog	5.0	2	False
cat	1.5	3	False
python	4.5	1	False
cat	NaN	1	True
dog	7.0	2	False
dog	3.0	1	False

21.對每種animal的每種不同數量visits，計算平均age，即，返回一個表格，行是aniaml種類，列是visits數量，表格值是行動物種類列訪客數量的平均年齡

df.dtypes animal???????object age?????????float64 visits???????object priority??????int64 dtype:?object df.age=df.age.astype(float) df.pivot_table(index='animal',?columns='visits',?values='age',?aggfunc='mean') visits123animal


catdogpython

2.5	NaN	2.25
3.0	6.0	NaN
4.5	0.5	NaN

22.在df中插入新行k，然后刪除該行

#插入 df.loc['k']?=?[5.5,?'dog',?'no',?2] #?刪除 df?=?df.drop('k') df
animalagevisitspriorityabcdefghij

cat	2.5	1	1
cat	3	3	1
python	0.5	2	0
dog	NaN	3	1
dog	5	2	0
cat	1.5	3	0
python	4.5	1	0
cat	NaN	1	1
dog	7	2	0
dog	3	1	0

進階操作

23.有一列整數列A的DatraFrame，刪除數值重復的行

df?=?pd.DataFrame({'A':?[1,?2,?2,?3,?4,?5,?5,?5,?6,?7,?7]}) print(df) df1?=?df.loc[df['A'].shift()?!=?df['A']] #?方法二 #?df1?=?df.drop_duplicates(subset='A') print(df1) ????A 0???1 1???2 2???2 3???3 4???4 5???5 6???5 7???5 8???6 9???7 10??7A 0??1 1??2 3??3 4??4 5??5 8??6 9??7

24.一個全數值DatraFrame，每個數字減去該行的平均數

df?=?pd.DataFrame(np.random.random(size=(5,?3))) print(df) df1?=?df.sub(df.mean(axis=1),?axis=0) print(df1) ??????????0?????????1?????????2 0??0.761859??0.579139??0.023214 1??0.520961??0.847583??0.044559 2??0.186740??0.561425??0.158097 3??0.606828??0.680284??0.903810 4??0.474712??0.404921??0.7765030?????????1?????????2 0??0.307122??0.124402?-0.431524 1??0.049927??0.376549?-0.426476 2?-0.115347??0.259337?-0.143990 3?-0.123479?-0.050023??0.173503 4?-0.077333?-0.147124??0.224457

25.一個有5列的DataFrame，求哪一列的和最小

df?=?pd.DataFrame(np.random.random(size=(5,?5)),?columns=list('abcde')) print(df) df.sum().idxmin() ??????????a?????????b?????????c?????????d?????????e 0??0.832332??0.947136??0.614947??0.162827??0.621645 1??0.786207??0.974123??0.675870??0.633438??0.960480 2??0.141939??0.070910??0.963475??0.055656??0.293488 3??0.279569??0.690073??0.570594??0.680619??0.706241 4??0.237010??0.145196??0.295304??0.628794??0.924545'd'

26.給定DataFrame，求A列每個值的前3大的B的和

df?=?pd.DataFrame({'A':?list('aaabbcaabcccbbc'),?'B':?[12,345,3,1,45,14,4,52,54,23,235,21,57,3,87]}) print(df) df1?=?df.groupby('A')['B'].nlargest(3).sum(level=0) print(df1) ????A????B 0???a???12 1???a??345 2???a????3 3???b????1 4???b???45 5???c???14 6???a????4 7???a???52 8???b???54 9???c???23 10??c??235 11??c???21 12??b???57 13??b????3 14??c???87 A a????409 b????156 c????345 Name:?B,?dtype:?int64

27.給定DataFrame，有列A, B，A的值在1-100（含），對A列每10步長，求對應的B的和

df?=?pd.DataFrame({'A':?[1,2,11,11,33,34,35,40,79,99],?'B':?[1,2,11,11,33,34,35,40,79,99]}) print(df) df1?=?df.groupby(pd.cut(df['A'],?np.arange(0,?101,?10)))['B'].sum() print(df1) ????A???B 0???1???1 1???2???2 2??11??11 3??11??11 4??33??33 5??34??34 6??35??35 7??40??40 8??79??79 9??99??99 A (0,?10]????????3 (10,?20]??????22 (20,?30]???????0 (30,?40]?????142 (40,?50]???????0 (50,?60]???????0 (60,?70]???????0 (70,?80]??????79 (80,?90]???????0 (90,?100]?????99 Name:?B,?dtype:?int64

28.給定DataFrame，計算每個元素至左邊最近的0（或者至開頭）的距離，生成新列y

df?=?pd.DataFrame({'X':?[7,?2,?0,?3,?4,?2,?5,?0,?3,?4]})izero?=?np.r_[-1,?(df['X']?==?0).to_numpy().nonzero()[0]]?#?標記0的位置 idx?=?np.arange(len(df)) df['Y']?=?idx?-?izero[np.searchsorted(izero?-?1,?idx)?-?1] print(df)#?方法二 #?x?=?(df['X']?!=?0).cumsum() #?y?=?x?!=?x.shift() #?df['Y']?=?y.groupby((y?!=?y.shift()).cumsum()).cumsum()#?方法三 #?df['Y']?=?df.groupby((df['X']?==?0).cumsum()).cumcount() #first_zero_idx?=?(df['X']?==?0).idxmax() #?df['Y'].iloc[0:first_zero_idx]?+=?1 ???X??Y 0??7??1 1??2??2 2??0??0 3??3??1 4??4??2 5??2??3 6??5??4 7??0??0 8??3??1 9??4??2

29.一個全數值的DataFrame，返回最大3值的坐標

df?=?pd.DataFrame(np.random.random(size=(5,?3))) print(df) df.unstack().sort_values()[-3:].index.tolist() ??????????0?????????1?????????2 0??0.322047??0.508559??0.481098 1??0.625304??0.582052??0.048630 2??0.848465??0.662735??0.038410 3??0.573324??0.664073??0.606389 4??0.920799??0.462395??0.684100[(2,?4),?(0,?2),?(0,?4)]

30.給定DataFrame，將負值代替為同組的平均值

df?=?pd.DataFrame({'grps':?list('aaabbcaabcccbbc'),?'vals':?[-12,345,3,1,45,14,4,-52,54,23,-235,21,57,3,87]}) print(df)def?replace(group):mask?=?group<0group[mask]?=?group[~mask].mean()return?groupdf['vals']?=?df.groupby(['grps'])['vals'].transform(replace) print(df) ???grps??vals 0?????a???-12 1?????a???345 2?????a?????3 3?????b?????1 4?????b????45 5?????c????14 6?????a?????4 7?????a???-52 8?????b????54 9?????c????23 10????c??-235 11????c????21 12????b????57 13????b?????3 14????c????87grps????????vals 0?????a??117.333333 1?????a??345.000000 2?????a????3.000000 3?????b????1.000000 4?????b???45.000000 5?????c???14.000000 6?????a????4.000000 7?????a??117.333333 8?????b???54.000000 9?????c???23.000000 10????c???36.250000 11????c???21.000000 12????b???57.000000 13????b????3.000000 14????c???87.000000

31.計算3位滑動窗口的平均值，忽略NAN

df?=?pd.DataFrame({'group':?list('aabbabbbabab'),'value':?[1,?2,?3,?np.nan,?2,?3,?np.nan,?1,?7,?3,?np.nan,?8]}) print(df)g1?=?df.groupby(['group'])['value'] g2?=?df.fillna(0).groupby(['group'])['value']?s?=?g2.rolling(3,?min_periods=1).sum()?/?g1.rolling(3,?min_periods=1).count()s.reset_index(level=0,?drop=True).sort_index()? ???group??value 0??????a????1.0 1??????a????2.0 2??????b????3.0 3??????b????NaN 4??????a????2.0 5??????b????3.0 6??????b????NaN 7??????b????1.0 8??????a????7.0 9??????b????3.0 10?????a????NaN 11?????b????8.00?????1.000000 1?????1.500000 2?????3.000000 3?????3.000000 4?????1.666667 5?????3.000000 6?????3.000000 7?????2.000000 8?????3.666667 9?????2.000000 10????4.500000 11????4.000000 Name:?value,?dtype:?float64

Series 和 Datetime索引

32.創建Series s，將2015所有工作日作為隨機值的索引

dti?=?pd.date_range(start='2015-01-01',?end='2015-12-31',?freq='B')? s?=?pd.Series(np.random.rand(len(dti)),?index=dti)s.head(10) 2015-01-01????0.542640 2015-01-02????0.843010 2015-01-05????0.335675 2015-01-06????0.823544 2015-01-07????0.416880 2015-01-08????0.587211 2015-01-09????0.805899 2015-01-12????0.824835 2015-01-13????0.639243 2015-01-14????0.406859 Freq:?B,?dtype:?float64

33.所有禮拜三的值求和

s[s.index.weekday?==?2].sum()? 24.085656574156896

34.求每個自然月的平均數

s.resample('M').mean() 2015-01-31????0.507893 2015-02-28????0.589465 2015-03-31????0.622884 2015-04-30????0.559272 2015-05-31????0.568332 2015-06-30????0.469618 2015-07-31????0.396087 2015-08-31????0.409175 2015-09-30????0.521630 2015-10-31????0.558678 2015-11-30????0.497789 2015-12-31????0.460043 Freq:?M,?dtype:?float64

35.每連續4個月為一組，求最大值所在的日期

s.groupby(pd.Grouper(freq='4M')).idxmax() 2015-01-31???2015-01-02 2015-05-31???2015-03-25 2015-09-30???2015-06-09 2016-01-31???2015-10-01 Freq:?4M,?dtype:?datetime64[ns]

36.創建2015-2016每月第三個星期四的序列

pd.date_range('2015-01-01',?'2016-12-31',?freq='WOM-3THU') DatetimeIndex(['2015-01-15',?'2015-02-19',?'2015-03-19',?'2015-04-16','2015-05-21',?'2015-06-18',?'2015-07-16',?'2015-08-20','2015-09-17',?'2015-10-15',?'2015-11-19',?'2015-12-17','2016-01-21',?'2016-02-18',?'2016-03-17',?'2016-04-21','2016-05-19',?'2016-06-16',?'2016-07-21',?'2016-08-18','2016-09-15',?'2016-10-20',?'2016-11-17',?'2016-12-15'],dtype='datetime64[ns]',?freq='WOM-3THU')

數據清洗

df?=?pd.DataFrame({'From_To':?['LoNDon_paris',?'MAdrid_miLAN',?'londON_StockhOlm',?'Budapest_PaRis',?'Brussels_londOn'],'FlightNumber':?[10045,?np.nan,?10065,?np.nan,?10085],'RecentDelays':?[[23,?47],?[],?[24,?43,?87],?[13],?[67,?32]],'Airline':?['KLM(!)',?'<Air?France>?(12)',?'(British?Airways.?)',?'12.?Air?France',?'"Swiss?Air"']}) df
From_ToFlightNumberRecentDelaysAirline01234

LoNDon_paris	10045.0	[23, 47]	KLM(!)
MAdrid_miLAN	NaN	[]	<Air France> (12)
londON_StockhOlm	10065.0	[24, 43, 87]	(British Airways. )
Budapest_PaRis	NaN	[13]	12. Air France
Brussels_londOn	10085.0	[67, 32]	"Swiss Air"

37.FlightNumber列中有些值缺失了，他們本來應該是每一行增加10，填充缺失的數值，并且令數據類型為整數

df['FlightNumber']?=?df['FlightNumber'].interpolate().astype(int) df
From_ToFlightNumberRecentDelaysAirline01234

LoNDon_paris	10045	[23, 47]	KLM(!)
MAdrid_miLAN	10055	[]	<Air France> (12)
londON_StockhOlm	10065	[24, 43, 87]	(British Airways. )
Budapest_PaRis	10075	[13]	12. Air France
Brussels_londOn	10085	[67, 32]	"Swiss Air"

38.將From_To列從_分開，分成From, To兩列，并刪除原始列

temp?=?df.From_To.str.split('_',?expand=True) temp.columns?=?['From',?'To'] df?=?df.join(temp) df?=?df.drop('From_To',?axis=1) df
FlightNumberRecentDelaysAirlineFromTo01234

10045	[23, 47]	KLM(!)	LoNDon	paris
10055	[]	<Air France> (12)	MAdrid	miLAN
10065	[24, 43, 87]	(British Airways. )	londON	StockhOlm
10075	[13]	12. Air France	Budapest	PaRis
10085	[67, 32]	"Swiss Air"	Brussels	londOn

39.將From, To大小寫統一

df['From']?=?df['From'].str.capitalize() df['To']?=?df['To'].str.capitalize() df
FlightNumberRecentDelaysAirlineFromTo01234

10045	[23, 47]	KLM(!)	London	Paris
10055	[]	<Air France> (12)	Madrid	Milan
10065	[24, 43, 87]	(British Airways. )	London	Stockholm
10075	[13]	12. Air France	Budapest	Paris
10085	[67, 32]	"Swiss Air"	Brussels	London

40.Airline列，有一些多余的標點符號，需要提取出正確的航司名稱。舉例：'(British Airways. )' 應該改為 'British Airways'.

df['Airline']?=?df['Airline'].str.extract('([a-zA-Z\s]+)',?expand=False).str.strip() df
FlightNumberRecentDelaysAirlineFromTo01234

10045	[23, 47]	KLM	London	Paris
10055	[]	Air France	Madrid	Milan
10065	[24, 43, 87]	British Airways	London	Stockholm
10075	[13]	Air France	Budapest	Paris
10085	[67, 32]	Swiss Air	Brussels	London

41.Airline列，數據被以列表的形式錄入，但是我們希望每個數字被錄入成單獨一列，delay_1, delay_2, ...沒有的用NAN替代。

delays?=?df['RecentDelays'].apply(pd.Series) delays.columns?=?['delay_{}'.format(n)?for?n?in?range(1,?len(delays.columns)+1)] df?=?df.drop('RecentDelays',?axis=1).join(delays)df
FlightNumberAirlineFromTodelay_1delay_2delay_301234

10045	KLM	London	Paris	23.0	47.0	NaN
10055	Air France	Madrid	Milan	NaN	NaN	NaN
10065	British Airways	London	Stockholm	24.0	43.0	87.0
10075	Air France	Budapest	Paris	13.0	NaN	NaN
10085	Swiss Air	Brussels	London	67.0	32.0	NaN

層次化索引

42.用 letters = ['A', 'B', 'C'] 和 numbers = list(range(10))的組合作為系列隨機值的層次化索引

letters?=?['A',?'B',?'C'] numbers?=?list(range(4))mi?=?pd.MultiIndex.from_product([letters,?numbers]) s?=?pd.Series(np.random.rand(12),?index=mi) s A??0????0.8678281????0.6336802????0.3283333????0.726590 B??0????0.9726931????0.1403612????0.9058363????0.072299 C??0????0.7879101????0.4080232????0.5322203????0.219010 dtype:?float64

43.檢查s是否是字典順序排序的

s.index.is_lexsorted() #?方法二 #?s.index.lexsort_depth?==?s.index.nlevels True

44.選擇二級索引為1, 3的行

s.loc[:,?[1,?3]] A??1????0.6336803????0.726590 B??1????0.1403613????0.072299 C??1????0.4080233????0.219010 dtype:?float64

45.對s進行切片操作，取一級索引從頭至B，二級索引從2開始到最后

s.loc[pd.IndexSlice[:'B',?2:]] #?方法二 #?s.loc[slice(None,?'B'),?slice(2,?None)] A??2????0.3283333????0.726590 B??2????0.9058363????0.072299 dtype:?float64

46.計算每個一級索引的和（A, B, C每一個的和）

s.sum(level=0) #方法二 #s.unstack().sum(axis=0) A????2.556431 B????2.091189 C????1.947163 dtype:?float64

47.交換索引等級，新的Series是字典順序嗎？不是的話請排序

new_s?=?s.swaplevel(0,?1) print(new_s) print(new_s.index.is_lexsorted()) new_s?=?new_s.sort_index() print(new_s) 0??A????0.867828 1??A????0.633680 2??A????0.328333 3??A????0.726590 0??B????0.972693 1??B????0.140361 2??B????0.905836 3??B????0.072299 0??C????0.787910 1??C????0.408023 2??C????0.532220 3??C????0.219010 dtype:?float64 False 0??A????0.867828B????0.972693C????0.787910 1??A????0.633680B????0.140361C????0.408023 2??A????0.328333B????0.905836C????0.532220 3??A????0.726590B????0.072299C????0.219010 dtype:?float64

可視化

import?matplotlib.pyplot?as?plt df?=?pd.DataFrame({"xs":[1,5,2,8,1],?"ys":[4,2,1,9,6]}) plt.style.use('ggplot')

48.畫出df的散點圖

df.plot.scatter("xs",?"ys",?color?=?"black",?marker?=?"x") <matplotlib.axes._subplots.AxesSubplot?at?0x7fbb753a8eb8>

49.可視化指定4維DataFrame

df?=?pd.DataFrame({"productivity":[5,2,3,1,4,5,6,7,8,3,4,8,9],"hours_in"????:[1,9,6,5,3,9,2,9,1,7,4,2,2],"happiness"???:[2,1,3,2,3,1,2,3,1,2,2,1,3],"caffienated"?:[0,0,1,1,0,0,0,0,1,1,0,1,0]})df.plot.scatter("hours_in",?"productivity",?s?=?df.happiness?*?100,?c?=?df.caffienated) <matplotlib.axes._subplots.AxesSubplot?at?0x7fbb752f5780>

50.在同一個圖中可視化2組數據，共用X軸，但y軸不同

df?=?pd.DataFrame({"revenue":[57,68,63,71,72,90,80,62,59,51,47,52],"advertising":[2.1,1.9,2.7,3.0,3.6,3.2,2.7,2.4,1.8,1.6,1.3,1.9],"month":range(12)})ax?=?df.plot.bar("month",?"revenue",?color?=?"green") df.plot.line("month",?"advertising",?secondary_y?=?True,?ax?=?ax) ax.set_xlim((-1,12));

參考資料：

• 100-pandas-puzzles - GitHub

• Pandas 百題大沖關

END

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