Bagaimana cara mencetak banyak kolom di excel dengan python?

################################################## . # # Pengenal-Lisensi-SPDX. BSD-2-Klausul # Hak Cipta 2013-2023, John McNamara, jmcnamara@cpan. org # impor panda sebagai pd # Buat beberapa kerangka data Panda dari beberapa data. df1 = pd. DataFrame({'Data'. [11, 12, 13, 14]}) df2 = pd. DataFrame({'Data'. [21, 22, 23, 24]}) df3 = pd. DataFrame({'Data'. [31, 32, 33, 34]}) # Buat penulis Pandas Excel menggunakan XlsxWriter sebagai mesin. penulis = pd. ExcelWriter('pandas_multiple. xlsx', engine='xlsxwriter') # Tulis setiap kerangka data ke lembar kerja yang berbeda. df1. to_excel(penulis, sheet_name='Sheet1') df2. to_excel(penulis, sheet_name='Sheet2') df3. to_excel(writer, sheet_name='Sheet3') # Tutup penulis Pandas Excel dan keluarkan file Excel. penulis. menutup()

CityColors ReportedShape ReportedStateTime0IthacaNaNTRIANGLENY6/1/1930 22. 001WillingboroNaNOTHERNJ6/30/1930 20. 002HolyokeNaNOVALCO2/15/1931 14. 00

penggunaan lokasi
Ini adalah metode yang sangat kuat dan fleksibel

Di [6]

# .loc DataFrame method # filtering rows and selecting columns by label # format # ufo.loc[rows, columns] # row 0, all columns ufo.loc[0, :]

Keluar[6]

City Ithaca Colors Reported NaN Shape Reported TRIANGLE State NY Time 6/1/1930 22:00 Name: 0, dtype: object_

Di [10]

# rows 0, 1, 2 # all columns ufo.loc[[0, 1, 2], :] # more efficient code ufo.loc[0:2, :]

Keluar[10]

CityColors ReportedShape ReportedStateTime0IthacaNaNTRIANGLENY6/1/1930 22. 001WillingboroNaNOTHERNJ6/30/1930 20. 002HolyokeNaNOVALCO2/15/1931 14. 00

Dalam [12]

# if you leave off ", :" pandas would assume it's there # but you should leave it there to improve code readability ufo.loc[0:2]

Keluar[12]

CityColors ReportedShape ReportedStateTime0IthacaNaNTRIANGLENY6/1/1930 22. 001WillingboroNaNOTHERNJ6/30/1930 20. 002HolyokeNaNOVALCO2/15/1931 14. 00

Di [13]

# all rows # column: City ufo.loc[:, 'City'] _

Keluar[13]

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

Di [15]

# all rows # column: City, State ufo.loc[:, ['City', 'State']] # similar code for City through State ufo.loc[:, 'City':'State'] _

Keluar[15]

CityColors ReportedShape ReportedState0IthacaNaNTRIANGLENY1WillingboroNaNOTHERNJ2HolyokeNaNOVALCO3AbileneNaNDISKKS4New York Worlds FairNaNLIGHTNY5Valley CityNaNDISKND6Crater LakeNaNCIRCLECA7AlmaNaNDISKMI8EklutnaNaNCIGARAK9HubbardNaNCYLINDEROR10FontanaNaNLIGHTCA11WaterlooNaNFIREBALLAL12BeltonREDSPHERESC13KeokukNaNOVALIA14LudingtonNaNDISKMI15Forest HomeNaNCIRCLECA16Los AngelesNaNNaNCA17HapevilleNaNNaNGA18OneidaNaNRECTANGLETN19Bering SeaREDOTHERAK20NebraskaNaNDISKNE21NaNNaNNaNLA22NaNNaNLIGHTLA23OwensboroNaNRECTANGLEKY24WildernessNaNDISKWV25San DiegoNaNCIGARCA26WildernessNaNDISKWV27ClovisNaNDISKNM28Los AlamosNaNDISKNM29Ft. DuscheneNaNDISKUT. 18211HolyokeNaNDIAMONDMA18212CarsonNaNDISKCA18213PasadenaGREENFIREBALLCA18214AustinNaNFORMATIONTX18215El CampoNaNOTHERTX18216Garden GroveORANGELIGHTCA18217Berthoud PassNaNTRIANGLECO18218SisterdaleNaNDIAMONDTX18219Garden GroveNaNCHEVRONCA18220Shasta LakeBLUEDISKCA18221FranklinNaNDISKNH18222AlbrightsvilleNaNOTHERPA18223GreenvilleNaNNaNSC18224EufaulaNaNDISKOK18225Simi ValleyNaNFORMATIONCA18226San FranciscoNaNFORMATIONCA18227San FranciscoNaNTRIANGLECA18228KingsvilleNaNLIGHTTX18229ChicagoNaNDISKIL18230Pismo BeachNaNOVALCA18231Pismo BeachNaNOVALCA18232LodiNaNNaNWI18233AnchorageREDVARIOUSAK18234CapitolaNaNTRIANGLECA18235Fountain HillsNaNNaNAZ18236Grant ParkNaNTRIANGLEIL18237Spirit LakeNaNDISKIA18238Eagle RiverNaNNaNWI18239Eagle RiverREDLIGHTWI18240YborNaNOVALFL

18241 baris × 4 kolom

Dalam [17]

url = '//bit.ly/uforeports' ufo = pd.read_csv(url) 0

Keluar[17]

CityColors ReportedShape ReportedState0IthacaNaNTRIANGLENY1WillingboroNaNOTHERNJ2HolyokeNaNOVALCO

Dalam [18]

url = '//bit.ly/uforeports' ufo = pd.read_csv(url) 1

Keluar[18]

CityColors ReportedShape ReportedStateTime1694OaklandNaNCIGARCA7/21/1968 14. 002144OaklandNaNDISKCA8/19/1971 0. 004686OaklandNaNLIGHTMD6/1/1982 0. 007293OaklandNaNLIGHTCA3/28/1994 17. 008488OaklandNaNNaNCA8/10/1995 21. 458768OaklandNaNNaNCA10/10/1995 22. 4010816OaklandNaNLIGHTOR10/1/1997 21. 3010948OaklandNaNDISKCA11/14/1997 19. 5511045OaklandNaNTRIANGLECA12/10/1997 1. 3012322OaklandNaNFIREBALLCA10/9/1998 19. 4012941OaklandNaNCYLINDERCA1/23/1999 21. 3016803OaklandNaNTRIANGLEMD7/4/2000 23. 0017322OaklandNaNCYLINDERCA9/1/2000 21. 35

Dalam [20]

url = '//bit.ly/uforeports' ufo = pd.read_csv(url) 2

Keluar[20]

CityColors ReportedShape ReportedStateTime1694OaklandNaNCIGARCA7/21/1968 14. 002144OaklandNaNDISKCA8/19/1971 0. 004686OaklandNaNLIGHTMD6/1/1982 0. 007293OaklandNaNLIGHTCA3/28/1994 17. 008488OaklandNaNNaNCA8/10/1995 21. 458768OaklandNaNNaNCA10/10/1995 22. 4010816OaklandNaNLIGHTOR10/1/1997 21. 3010948OaklandNaNDISKCA11/14/1997 19. 5511045OaklandNaNTRIANGLECA12/10/1997 1. 3012322OaklandNaNFIREBALLCA10/9/1998 19. 4012941OaklandNaNCYLINDERCA1/23/1999 21. 3016803OaklandNaNTRIANGLEMD7/4/2000 23. 0017322OaklandNaNCYLINDERCA9/1/2000 21. 35

Dalam [21]

url = '//bit.ly/uforeports' ufo = pd.read_csv(url) 3

Keluar[21]

url = '//bit.ly/uforeports' ufo = pd.read_csv(url) 4

Di [24]

url = '//bit.ly/uforeports' ufo = pd.read_csv(url) 5

Keluar[24]

url = '//bit.ly/uforeports' ufo = pd.read_csv(url) 4

penggunaan iloc

Di [25]

url = '//bit.ly/uforeports' ufo = pd.read_csv(url) 7

Keluar[25]

CityState0IthacaNY1WillingboroNJ2HolyokeCO3AbileneKS4New York Worlds FairNY5Valley CityND6Crater LakeCA7AlmaMI8EklutnaAK9HubbardOR10FontanaCA11WaterlooAL12BeltonSC13KeokukIA14LudingtonMI15Forest HomeCA16Los AngelesCA17HapevilleGA18OneidaTN19Bering SeaAK20NebraskaNE21NaNLA22NaNLA23OwensboroKY24WildernessWV25San DiegoCA26WildernessWV27ClovisNM28Los AlamosNM29Ft. DuscheneUT. 18211HolyokeMA18212CarsonCA18213PasadenaCA18214AustinTX18215El CampoTX18216Garden GroveCA18217Berthoud PassCO18218SisterdaleTX18219Garden GroveCA18220Shasta LakeCA18221FranklinNH18222AlbrightsvillePA18223GreenvilleSC18224EufaulaOK18225Simi ValleyCA18226San FranciscoCA18227San FranciscoCA18228KingsvilleTX18229ChicagoIL18230Pismo BeachCA18231Pismo BeachCA18232LodiWI18233AnchorageAK18234CapitolaCA18235Fountain HillsAZ18236Grant ParkIL18237Spirit LakeIA18238Eagle RiverWI18239Eagle RiverWI18240YborFL

18241 baris × 2 kolom

Di [28]

url = '//bit.ly/uforeports' ufo = pd.read_csv(url) _8

Keluar[28]

CityColors ReportedShape ReportedState0IthacaNaNTRIANGLENY1WillingboroNaNOTHERNJ2HolyokeNaNOVALCO3AbileneNaNDISKKS4New York Worlds FairNaNLIGHTNY5Valley CityNaNDISKND6Crater LakeNaNCIRCLECA7AlmaNaNDISKMI8EklutnaNaNCIGARAK9HubbardNaNCYLINDEROR10FontanaNaNLIGHTCA11WaterlooNaNFIREBALLAL12BeltonREDSPHERESC13KeokukNaNOVALIA14LudingtonNaNDISKMI15Forest HomeNaNCIRCLECA16Los AngelesNaNNaNCA17HapevilleNaNNaNGA18OneidaNaNRECTANGLETN19Bering SeaREDOTHERAK20NebraskaNaNDISKNE21NaNNaNNaNLA22NaNNaNLIGHTLA23OwensboroNaNRECTANGLEKY24WildernessNaNDISKWV25San DiegoNaNCIGARCA26WildernessNaNDISKWV27ClovisNaNDISKNM28Los AlamosNaNDISKNM29Ft. DuscheneNaNDISKUT. 18211HolyokeNaNDIAMONDMA18212CarsonNaNDISKCA18213PasadenaGREENFIREBALLCA18214AustinNaNFORMATIONTX18215El CampoNaNOTHERTX18216Garden GroveORANGELIGHTCA18217Berthoud PassNaNTRIANGLECO18218SisterdaleNaNDIAMONDTX18219Garden GroveNaNCHEVRONCA18220Shasta LakeBLUEDISKCA18221FranklinNaNDISKNH18222AlbrightsvilleNaNOTHERPA18223GreenvilleNaNNaNSC18224EufaulaNaNDISKOK18225Simi ValleyNaNFORMATIONCA18226San FranciscoNaNFORMATIONCA18227San FranciscoNaNTRIANGLECA18228KingsvilleNaNLIGHTTX18229ChicagoNaNDISKIL18230Pismo BeachNaNOVALCA18231Pismo BeachNaNOVALCA18232LodiNaNNaNWI18233AnchorageREDVARIOUSAK18234CapitolaNaNTRIANGLECA18235Fountain HillsNaNNaNAZ18236Grant ParkNaNTRIANGLEIL18237Spirit LakeNaNDISKIA18238Eagle RiverNaNNaNWI18239Eagle RiverREDLIGHTWI18240YborNaNOVALFL

18241 baris × 4 kolom

Dalam [31]

url = '//bit.ly/uforeports' ufo = pd.read_csv(url) _9

Keluar[31]

CityColors ReportedShape ReportedStateTime0IthacaNaNTRIANGLENY6/1/1930 22. 001WillingboroNaNOTHERNJ6/30/1930 20. 002HolyokeNaNOVALCO2/15/1931 14. 00

Di [38]

# show first 3 shows ufo.head(3) 0

Keluar[38]

CityState0IthacaNY1WillingboroNJ2HolyokeCO3AbileneKS4New York Worlds FairNY5Valley CityND6Crater LakeCA7AlmaMI8EklutnaAK9HubbardOR10FontanaCA11WaterlooAL12BeltonSC13KeokukIA14LudingtonMI15Forest HomeCA16Los AngelesCA17HapevilleGA18OneidaTN19Bering SeaAK20NebraskaNE21NaNLA22NaNLA23OwensboroKY24WildernessWV25San DiegoCA26WildernessWV27ClovisNM28Los AlamosNM29Ft. DuscheneUT. 18211HolyokeMA18212CarsonCA18213PasadenaCA18214AustinTX18215El CampoTX18216Garden GroveCA18217Berthoud PassCO18218SisterdaleTX18219Garden GroveCA18220Shasta LakeCA18221FranklinNH18222AlbrightsvillePA18223GreenvilleSC18224EufaulaOK18225Simi ValleyCA18226San FranciscoCA18227San FranciscoCA18228KingsvilleTX18229ChicagoIL18230Pismo BeachCA18231Pismo BeachCA18232LodiWI18233AnchorageAK18234CapitolaCA18235Fountain HillsAZ18236Grant ParkIL18237Spirit LakeIA18238Eagle RiverWI18239Eagle RiverWI18240YborFL

18241 baris × 2 kolom

Di [40]

# show first 3 shows ufo.head(3) 1

Keluar[40]

CityColors ReportedShape ReportedStateTime0IthacaNaNTRIANGLENY6/1/1930 22. 001WillingboroNaNOTHERNJ6/30/1930 20. 00

ix penggunaan
Campurkan label dan bilangan bulat saat menggunakan pilihan

Di [41]

# show first 3 shows ufo.head(3) 2

Dalam [42]

# show first 3 shows ufo.head(3) 3

Keluar[42]

beer_servingsspirit_servingswine_servingstotal_litres_of_pure_alcoholcontinentcountryAfghanistan0000. 0AsiaAlbania89132544. 9EropaAljazair250140. 7AfrikaAndorra24513831212. 4EropaAngola21757455. 9Afrika

Bagaimana cara mencetak daftar beberapa kolom di Excel?

Dengan asumsi Anda memiliki daftar data dalam satu kolom yang ingin Anda cetak dalam banyak kolom. .
Pilih sel yang ingin Anda cetak
Klik File, lalu klik Cetak. Kotak dialog Cetak muncul
Di area Pengaturan, klik Kolom, lalu klik jumlah kolom yang Anda inginkan
Klik Oke

Bagaimana cara menampilkan banyak kolom dengan Python?

Ada tiga metode dasar yang dapat Anda gunakan untuk memilih beberapa kolom dari DataFrame panda. .
Metode 1. Pilih Kolom berdasarkan Indeks df_new = df. iloc[. , [0,1,3]]
Metode 2. Pilih Kolom dalam Kisaran Indeks df_new = df. iloc[. , 0. 3]
Metode 3. Pilih Kolom berdasarkan Nama df_new = df[['col1', 'col2']]

Bagaimana cara mendapatkan banyak kolom di panda?

Untuk memilih beberapa kolom, ekstrak dan tampilkan sesudahnya. df adalah bingkai data yang sebelumnya bernama. Kemudian buat bingkai data baru df1 , dan pilih kolom A hingga D yang ingin Anda ekstrak dan lihat. Semua kolom yang diperlukan akan muncul.

Bagaimana Anda mencetak kolom data dengan Python?

3 Cara Mudah Mencetak Nama Kolom dengan Python .
Menggunakan panda. kerangka data. kolom untuk mencetak nama kolom dengan Python. .
Menggunakan panda. kerangka data. kolom. .
Metode Python sort() untuk mendapatkan nama kolom. Metode Python sort() dapat digunakan untuk mendapatkan daftar nama kolom dari kerangka data dalam urutan kolom yang menaik

Postingan terbaru

LIHAT SEMUA