Datoteke CSV so datoteke, ločene z vejicami. Za dostop do podatkov iz datoteke CSV potrebujemo funkcijo read_csv() iz Pande, ki pridobi podatke v obliki podatkovnega okvira.
Sintaksa read_csv()
Tukaj je Pande berejo CSV sintakso s svojimi parametri.
Sintaksa: pd.read_csv (filepath_or_buffer, sep=’ ,’ , header=’infer’, index_col=Brez, usecols=Brez, engine=Brez, skiprows=Brez, nrows=Brez)
Parametri:
- filepath_ali_buffer : Lokacija datoteke csv. Sprejme katero koli pot niza ali URL datoteke.
- sep : pomeni ločilo, privzeto je ', '.
- glava : Sprejema int, seznam int, številke vrstic za uporabo kot imena stolpcev in začetek podatkov. Če ni posredovano nobeno ime, tj. header=None, bo prvi stolpec prikazan kot 0, drugi kot 1 in tako naprej.
- usecols : Pridobi samo izbrane stolpce iz datoteke CSV.
- nrows : Število vrstic, ki bodo prikazane iz nabora podatkov.
- stolpec_indeksa : Če ni, skupaj z zapisi ni prikazanih indeksnih številk.
- skiprows : preskoči prejete vrstice v novem podatkovnem okviru.
Preberite datoteko CSV s programom Panda read_csv
Pred uporabo te funkcije moramo uvoziti Pande knjižnico, bomo datoteko CSV naložili s programom Pandas.
PYTHON3
skener v Javi
# Import pandas> import> pandas as pd> # reading csv file> df>=> pd.read_csv(>'people.csv'>)> print>(df.head())> |
>
>
Izhod:
First Name Last Name Sex Email Date of birth Job Title 0 Shelby Terrell Male [email protected] 1945-10-26 Games developer 1 Phillip Summers Female [email protected] 1910-03-24 Phytotherapist 2 Kristine Travis Male [email protected] 1992-07-02 Homeopath 3 Yesenia Martinez Male [email protected] 2017-08-03 Market researcher 4 Lori Todd Male [email protected] 1938-12-01 Veterinary surgeon>
Uporaba sep v read_csv()
V tem primeru bomo vzeli datoteko CSV in nato dodali nekaj posebnih znakov, da vidimo, kako sep parameter deluje.
Python3
# sample = 'totalbill_tip, sex:smoker, day_time, size> # 16.99, 1.01:Female|No, Sun, Dinner, 2> # 10.34, 1.66, Male, No|Sun:Dinner, 3> # 21.01:3.5_Male, No:Sun, Dinner, 3> #23.68, 3.31, Male|No, Sun_Dinner, 2> # 24.59:3.61, Female_No, Sun, Dinner, 4> # 25.29, 4.71|Male, No:Sun, Dinner, 4'> # Importing pandas library> import> pandas as pd> # Load the data of csv> df>=> pd.read_csv(>'sample.csv'>,> >sep>=>'[:, |_]'>,> >engine>=>'python'>)> # Print the Dataframe> print>(df)> |
>
>
Izhod:
totalbill tip Unnamed: 2 sex smoker Unnamed: 5 day time Unnamed: 8 size 16.99 NaN 1.01 Female No NaN Sun NaN Dinner NaN 2 10.34 NaN 1.66 NaN Male NaN No Sun Dinner NaN 3 21.01 3.50 Male NaN No Sun NaN Dinner NaN 3.0 None 23.68 NaN 3.31 NaN Male No NaN Sun Dinner NaN 2 24.59 3.61 NaN Female No NaN Sun NaN Dinner NaN 2 25.29 NaN 4.71 Male NaN No Sun NaN Dinner NaN 4>
Uporaba usecols v read_csv()
Tukaj določamo samo 3 stolpce, tj. [Ime, spol, e-pošta] za nalaganje, in uporabljamo glavo 0 kot privzeto glavo.
Python3
df>=> pd.read_csv(>'people.csv'>,> >header>=>0>,> >usecols>=>[>'First Name'>,>'Sex'>,>'Email'>])> # printing dataframe> print>(df.head())> |
>
>
Izhod:
First Name Sex Email 0 Shelby Male [email protected] 1 Phillip Female [email protected] 2 Kristine Male [email protected] 3 Yesenia Male [email protected] 4 Lori Male [email protected]>
Uporaba index_col v read_csv()
Tukaj uporabljamo Seks najprej indeks in nato Naziv delovnega mesta index, lahko preprosto ponovno indeksiramo glavo z stolpec_indeksa parameter.
Python3
meni z nastavitvami telefona Android
df>=> pd.read_csv(>'people.csv'>,> >header>=>0>,> >index_col>=>[>'Sex'>,>'Job Title'>],> >usecols>=>[>'Sex'>,>'Job Title'>,>'Email'>])> print>(df.head())> |
>
>
Izhod:
Email Sex Job Title Male Games developer [email protected] Female Phytotherapist [email protected] Male Homeopath [email protected] Market researcher [email protected] Veterinary surgeon [email protected]>
Uporaba nrows v read_csv()
Tukaj samo prikažemo samo 5 vrstic z uporabo parameter nrows .
Python3
df>=> pd.read_csv(>'people.csv'>,> >header>=>0>,> >index_col>=>[>'Sex'>,>'Job Title'>],> >usecols>=>[>'Sex'>,>'Job Title'>,>'Email'>],> >nrows>=>3>)> print>(df)> |
>
>
Izhod:
Email Sex Job Title Male Games developer [email protected] Female Phytotherapist [email protected] Male Homeopath [email protected]>
Uporaba skiprows v read_csv()
The skiprows pomoč pri preskoku nekaterih vrstic v CSV, tj. tukaj boste opazili, da so bile vrstice, omenjene v skiprows, preskočene iz izvirnega nabora podatkov.
Python3
Logika 1. reda
df>=> pd.read_csv(>'people.csv'>)> print>(>'Previous Dataset: '>)> print>(df)> # using skiprows> df>=> pd.read_csv(>'people.csv'>, skiprows>=> [>1>,>5>])> print>(>'Dataset After skipping rows: '>)> print>(df)> |
>
>
Izhod:
Previous Dataset: First Name Last Name Sex Email Date of birth Job Title 0 Shelby Terrell Male [email protected] 1945-10-26 Games developer 1 Phillip Summers Female [email protected] 1910-03-24 Phytotherapist 2 Kristine Travis Male [email protected] 1992-07-02 Homeopath 3 Yesenia Martinez Male [email protected] 2017-08-03 Market researcher 4 Lori Todd Male [email protected] 1938-12-01 Veterinary surgeon 5 Erin Day Male [email protected] 2015-10-28 Management officer 6 Katherine Buck Female [email protected] 1989-01-22 Analyst 7 Ricardo Hinton Male [email protected] 1924-03-26 Hydrogeologist Dataset After skipping rows: First Name Last Name Sex Email Date of birth Job Title 0 Shelby Terrell Male [email protected] 1945-10-26 Games developer 1 Kristine Travis Male [email protected] 1992-07-02 Homeopath 2 Yesenia Martinez Male [email protected] 2017-08-03 Market researcher 3 Lori Todd Male [email protected] 1938-12-01 Veterinary surgeon 4 Katherine Buck Female [email protected] 1989-01-22 Analyst 5 Ricardo Hinton Male [email protected] 1924-03-26 Hydrogeologist>