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

Keluar[17]

CityColors ReportedShape ReportedState0IthacaNaNTRIANGLENY1WillingboroNaNOTHERNJ2HolyokeNaNOVALCO

Dalam [18]

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

Keluar[21]

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

Di [24]

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

Keluar[24]

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

penggunaan iloc

Di [25]

url = 'http://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 = 'http://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 = 'http://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]

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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