How do I select a pandas Series from a DataFrame?¶

单词：

1.series[’sɪriz] n一连串; 一系列 系列节目(同音Siri apple哈哈)

In [2]: import pandas as pd

In [5]: ufo = pd.read_table('http://bit.ly/uforeports',sep=',') """ 由于uforeports是.csv文件，所以可以用专门读取csv文件的方法进行读取 .csv文件 逗号分隔符文件 ufo = pd.read_csv('http://bit.ly/uforeports') """ ufo1 = pd.read_csv('http://bit.ly/uforeports')

In [4]: ufo.head()

Out[4]: CityColors ReportedShape ReportedStateTime0IthacaNaNTRIANGLENY6/1/1930 22:001WillingboroNaNOTHERNJ6/30/1930 20:002HolyokeNaNOVALCO2/15/1931 14:003AbileneNaNDISKKS6/1/1931 13:004New York Worlds FairNaNLIGHTNY4/18/1933 19:00

In [6]: ufo1.head() #测试后结果一样 哈哈哈。。。。

Out[6]: CityColors ReportedShape ReportedStateTime0IthacaNaNTRIANGLENY6/1/1930 22:001WillingboroNaNOTHERNJ6/30/1930 20:002HolyokeNaNOVALCO2/15/1931 14:003AbileneNaNDISKKS6/1/1931 13:004New York Worlds FairNaNLIGHTNY4/18/1933 19:00

In [7]: type(ufo) #被pandas读取后文件的类型是DataFrame类型

Out[7]: pandas.core.frame.DataFrame

In [9]: #DataFrame类型数据进行操作 ufo['City']

Out[9]: 0 Ithaca 1 Willingboro 2 Holyoke 3 Abilene 4 New York Worlds Fair 5 Valley City 6 Crater Lake 7 Alma 8 Eklutna 9 Hubbard 10 Fontana 11 Waterloo 12 Belton 13 Keokuk 14 Ludington 15 Forest Home 16 Los Angeles 17 Hapeville 18 Oneida 19 Bering Sea 20 Nebraska 21 NaN 22 NaN 23 Owensboro 24 Wilderness 25 San Diego 26 Wilderness 27 Clovis 28 Los Alamos 29 Ft. Duschene ... 18211 Holyoke 18212 Carson 18213 Pasadena 18214 Austin 18215 El Campo 18216 Garden Grove 18217 Berthoud Pass 18218 Sisterdale 18219 Garden Grove 18220 Shasta Lake 18221 Franklin 18222 Albrightsville 18223 Greenville 18224 Eufaula 18225 Simi Valley 18226 San Francisco 18227 San Francisco 18228 Kingsville 18229 Chicago 18230 Pismo Beach 18231 Pismo Beach 18232 Lodi 18233 Anchorage 18234 Capitola 18235 Fountain Hills 18236 Grant Park 18237 Spirit Lake 18238 Eagle River 18239 Eagle River 18240 Ybor Name: City, dtype: object

In [10]: type(ufo['City']) #由此可以看出DataFrame类型 是由多个 Series类构成的

Out[10]: pandas.core.series.Series

In [11]: ufo.City #这种方式比 ufo['City'] 快

Out[11]: 0 Ithaca 1 Willingboro 2 Holyoke 3 Abilene 4 New York Worlds Fair 5 Valley City 6 Crater Lake 7 Alma 8 Eklutna 9 Hubbard 10 Fontana 11 Waterloo 12 Belton 13 Keokuk 14 Ludington 15 Forest Home 16 Los Angeles 17 Hapeville 18 Oneida 19 Bering Sea 20 Nebraska 21 NaN 22 NaN 23 Owensboro 24 Wilderness 25 San Diego 26 Wilderness 27 Clovis 28 Los Alamos 29 Ft. Duschene ... 18211 Holyoke 18212 Carson 18213 Pasadena 18214 Austin 18215 El Campo 18216 Garden Grove 18217 Berthoud Pass 18218 Sisterdale 18219 Garden Grove 18220 Shasta Lake 18221 Franklin 18222 Albrightsville 18223 Greenville 18224 Eufaula 18225 Simi Valley 18226 San Francisco 18227 San Francisco 18228 Kingsville 18229 Chicago 18230 Pismo Beach 18231 Pismo Beach 18232 Lodi 18233 Anchorage 18234 Capitola 18235 Fountain Hills 18236 Grant Park 18237 Spirit Lake 18238 Eagle River 18239 Eagle River 18240 Ybor Name: City, dtype: object

In [12]: ufo.Colors Reported #如果名字中有空格这样访问会出错误

File "<ipython-input-12-b711e91473ca>", line 1 ufo.Colors Reported ^ SyntaxError: invalid syntax

In [13]: ufo['Colors Reported'] # 颜色 报告【颜色表示】

Out[13]: 0 NaN 1 NaN 2 NaN 3 NaN 4 NaN 5 NaN 6 NaN 7 NaN 8 NaN 9 NaN 10 NaN 11 NaN 12 RED 13 NaN 14 NaN 15 NaN 16 NaN 17 NaN 18 NaN 19 RED 20 NaN 21 NaN 22 NaN 23 NaN 24 NaN 25 NaN 26 NaN 27 NaN 28 NaN 29 NaN ... 18211 NaN 18212 NaN 18213 GREEN 18214 NaN 18215 NaN 18216 ORANGE 18217 NaN 18218 NaN 18219 NaN 18220 BLUE 18221 NaN 18222 NaN 18223 NaN 18224 NaN 18225 NaN 18226 NaN 18227 NaN 18228 NaN 18229 NaN 18230 NaN 18231 NaN 18232 NaN 18233 RED 18234 NaN 18235 NaN 18236 NaN 18237 NaN 18238 NaN 18239 RED 18240 NaN Name: Colors Reported, dtype: object

In [14]: ufo.shape

Out[14]: (18241, 5)

In [15]: 'ab' + 'cd'

Out[15]: 'abcd'

In [16]: #Series 也有类似的特性 ufo.City + ufo.State

Out[16]: 0 IthacaNY 1 WillingboroNJ 2 HolyokeCO 3 AbileneKS 4 New York Worlds FairNY 5 Valley CityND 6 Crater LakeCA 7 AlmaMI 8 EklutnaAK 9 HubbardOR 10 FontanaCA 11 WaterlooAL 12 BeltonSC 13 KeokukIA 14 LudingtonMI 15 Forest HomeCA 16 Los AngelesCA 17 HapevilleGA 18 OneidaTN 19 Bering SeaAK 20 NebraskaNE 21 NaN 22 NaN 23 OwensboroKY 24 WildernessWV 25 San DiegoCA 26 WildernessWV 27 ClovisNM 28 Los AlamosNM 29 Ft. DuscheneUT ... 18211 HolyokeMA 18212 CarsonCA 18213 PasadenaCA 18214 AustinTX 18215 El CampoTX 18216 Garden GroveCA 18217 Berthoud PassCO 18218 SisterdaleTX 18219 Garden GroveCA 18220 Shasta LakeCA 18221 FranklinNH 18222 AlbrightsvillePA 18223 GreenvilleSC 18224 EufaulaOK 18225 Simi ValleyCA 18226 San FranciscoCA 18227 San FranciscoCA 18228 KingsvilleTX 18229 ChicagoIL 18230 Pismo BeachCA 18231 Pismo BeachCA 18232 LodiWI 18233 AnchorageAK 18234 CapitolaCA 18235 Fountain HillsAZ 18236 Grant ParkIL 18237 Spirit LakeIA 18238 Eagle RiverWI 18239 Eagle RiverWI 18240 YborFL dtype: object

In [18]: ufo.City + ', ' + ufo.State

Out[18]: 0 Ithaca, NY 1 Willingboro, NJ 2 Holyoke, CO 3 Abilene, KS 4 New York Worlds Fair, NY 5 Valley City, ND 6 Crater Lake, CA 7 Alma, MI 8 Eklutna, AK 9 Hubbard, OR 10 Fontana, CA 11 Waterloo, AL 12 Belton, SC 13 Keokuk, IA 14 Ludington, MI 15 Forest Home, CA 16 Los Angeles, CA 17 Hapeville, GA 18 Oneida, TN 19 Bering Sea, AK 20 Nebraska, NE 21 NaN 22 NaN 23 Owensboro, KY 24 Wilderness, WV 25 San Diego, CA 26 Wilderness, WV 27 Clovis, NM 28 Los Alamos, NM 29 Ft. Duschene, UT ... 18211 Holyoke, MA 18212 Carson, CA 18213 Pasadena, CA 18214 Austin, TX 18215 El Campo, TX 18216 Garden Grove, CA 18217 Berthoud Pass, CO 18218 Sisterdale, TX 18219 Garden Grove, CA 18220 Shasta Lake, CA 18221 Franklin, NH 18222 Albrightsville, PA 18223 Greenville, SC 18224 Eufaula, OK 18225 Simi Valley, CA 18226 San Francisco, CA 18227 San Francisco, CA 18228 Kingsville, TX 18229 Chicago, IL 18230 Pismo Beach, CA 18231 Pismo Beach, CA 18232 Lodi, WI 18233 Anchorage, AK 18234 Capitola, CA 18235 Fountain Hills, AZ 18236 Grant Park, IL 18237 Spirit Lake, IA 18238 Eagle River, WI 18239 Eagle River, WI 18240 Ybor, FL dtype: object

In [21]: ufo.Location = ufo.City + ', ' + ufo.State #没有作用

In [22]: ufo.head()

Out[22]: CityColors ReportedShape ReportedStateTime0IthacaNaNTRIANGLENY6/1/1930 22:001WillingboroNaNOTHERNJ6/30/1930 20:002HolyokeNaNOVALCO2/15/1931 14:003AbileneNaNDISKKS6/1/1931 13:004New York Worlds FairNaNLIGHTNY4/18/1933 19:00

In [25]: ufo.Location

Out[25]: 0 Ithaca, NY 1 Willingboro, NJ 2 Holyoke, CO 3 Abilene, KS 4 New York Worlds Fair, NY 5 Valley City, ND 6 Crater Lake, CA 7 Alma, MI 8 Eklutna, AK 9 Hubbard, OR 10 Fontana, CA 11 Waterloo, AL 12 Belton, SC 13 Keokuk, IA 14 Ludington, MI 15 Forest Home, CA 16 Los Angeles, CA 17 Hapeville, GA 18 Oneida, TN 19 Bering Sea, AK 20 Nebraska, NE 21 NaN 22 NaN 23 Owensboro, KY 24 Wilderness, WV 25 San Diego, CA 26 Wilderness, WV 27 Clovis, NM 28 Los Alamos, NM 29 Ft. Duschene, UT ... 18211 Holyoke, MA 18212 Carson, CA 18213 Pasadena, CA 18214 Austin, TX 18215 El Campo, TX 18216 Garden Grove, CA 18217 Berthoud Pass, CO 18218 Sisterdale, TX 18219 Garden Grove, CA 18220 Shasta Lake, CA 18221 Franklin, NH 18222 Albrightsville, PA 18223 Greenville, SC 18224 Eufaula, OK 18225 Simi Valley, CA 18226 San Francisco, CA 18227 San Francisco, CA 18228 Kingsville, TX 18229 Chicago, IL 18230 Pismo Beach, CA 18231 Pismo Beach, CA 18232 Lodi, WI 18233 Anchorage, AK 18234 Capitola, CA 18235 Fountain Hills, AZ 18236 Grant Park, IL 18237 Spirit Lake, IA 18238 Eagle River, WI 18239 Eagle River, WI 18240 Ybor, FL dtype: object

In [23]: #当要创建新一栏时并且添加到ufo的DataFrame中去，必须使用如下形式 ufo['Location'] = ufo.City + ', ' + ufo.State

In [24]: ufo.head()

Out[24]: CityColors ReportedShape ReportedStateTimeLocation0IthacaNaNTRIANGLENY6/1/1930 22:00Ithaca, NY1WillingboroNaNOTHERNJ6/30/1930 20:00Willingboro, NJ2HolyokeNaNOVALCO2/15/1931 14:00Holyoke, CO3AbileneNaNDISKKS6/1/1931 13:00Abilene, KS4New York Worlds FairNaNLIGHTNY4/18/1933 19:00New York Worlds Fair, NY