Pandas DataFrame je dvodimenzionalna velikostno spremenljiva, potencialno heterogena tabelarična podatkovna struktura z označenimi osmi (vrstice in stolpci). Aritmetične operacije se poravnajo na oznakah vrstic in stolpcev. Lahko si ga predstavljamo kot vsebnik, podoben dictu, za objekte serije. To je primarna podatkovna struktura Pande .
Sintaksa Pandas DataFrame loc[].
Pande DataFrame.loc dostopa do skupine vrstic in stolpcev po oznaki ali logični matriki v danem Pandas DataFrame .
Sintaksa: DataFrame.loc
Parameter: Noben
Vrnitve: Skalar, serije, DataFrame
Lastnost Pandas DataFrame loc
Spodaj je nekaj primerov, s katerimi lahko uporabimo Pandas DataFrame loc[]:
Primer 1: Izberite eno vrstico in stolpec po oznaki z uporabo loc[]
Uporabite atribut DataFrame.loc za dostop do določene celice v dani Pandas Dataframe z uporabo indeksa in oznak stolpcev. Nato izberemo eno vrstico in stolpec po oznaki z uporabo loc[].
Python3
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# importing pandas as pd> import> pandas as pd> # Creating the DataFrame> df>=> pd.DataFrame({>'Weight'>: [>45>,>88>,>56>,>15>,>71>],> >'Name'>: [>'Sam'>,>'Andrea'>,>'Alex'>,>'Robin'>,>'Kia'>],> >'Age'>: [>14>,>25>,>55>,>8>,>21>]})> # Create the index> index_>=> [>'Row_1'>,>'Row_2'>,>'Row_3'>,>'Row_4'>,>'Row_5'>]> # Set the index> df.index>=> index_> # Print the DataFrame> print>(>'Original DataFrame:'>)> print>(df)> # Corrected selection using loc for a specific cell> result>=> df.loc[>'Row_2'>,>'Name'>]> # Print the result> print>(>'
Selected Value at Row_2, Column 'Name':'>)> print>(result)> |
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Izhod
Original DataFrame: Weight Name Age Row_1 45 Sam 14 Row_2 88 Andrea 25 Row_3 56 Alex 55 Row_4 15 Robin 8 Row_5 71 Kia 21 Selected Value at Row_2, Column 'Name': Andrea>
Primer 2: Izberite Več vrstic in stolpcev
Uporabite atribut DataFrame.loc, da vrnete dva stolpca v danem Dataframeu in nato izberete več vrstic in stolpcev, kot je storjeno v spodnjem primeru.
Python3
# importing pandas as pd> import> pandas as pd> # Creating the DataFrame> df>=> pd.DataFrame({>'A'>:[>12>,>4>,>5>,>None>,>1>],> >'B'>:[>7>,>2>,>54>,>3>,>None>],> >'C'>:[>20>,>16>,>11>,>3>,>8>],> >'D'>:[>14>,>3>,>None>,>2>,>6>]})> # Create the index> index_>=> [>'Row_1'>,>'Row_2'>,>'Row_3'>,>'Row_4'>,>'Row_5'>]> # Set the index> df.index>=> index_> # Print the DataFrame> print>(>'Original DataFrame:'>)> print>(df)> # Corrected column names ('A' and 'D') in the result> result>=> df.loc[:, [>'A'>,>'D'>]]> # Print the result> print>(>'
Selected Columns 'A' and 'D':'>)> print>(result)> |
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Izhod
Original DataFrame: A B C D Row_1 12.0 7 20 14.0 Row_2 4.0 2 16 3.0 Row_3 5.0 54 11 NaN Row_4 NaN 3 3 2.0 Row_5 1.0 NaN 8 6.0 Selected Columns 'A' and 'D': A D Row_1 12.0 14.0 Row_2 4.0 3.0 Row_3 5.0 NaN Row_4 NaN 2.0 Row_5 1.0 6.0>
3. primer: izbira med dvema vrsticama ali stolpcema
V tem primeru ustvarimo pandas DataFrame z imenom 'df', nastavimo indekse vrstic po meri in nato uporabimoloc>dostopnik za izbiro vrstic med vključno »Row_2« in »Row_4« ter stolpce od »B« do »D«. Izbrane vrstice in stolpci so natisnjeni, kar prikazuje uporabo indeksiranja na podlagi oznak zloc>.
Python3
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# importing pandas as pd> import> pandas as pd> # Creating the DataFrame> df>=> pd.DataFrame({>'A'>: [>12>,>4>,>5>,>None>,>1>],> >'B'>: [>7>,>2>,>54>,>3>,>None>],> >'C'>: [>20>,>16>,>11>,>3>,>8>],> >'D'>: [>14>,>3>,>None>,>2>,>6>]})> # Create the index> index_>=> [>'Row_1'>,>'Row_2'>,>'Row_3'>,>'Row_4'>,>'Row_5'>]> # Set the index> df.index>=> index_> # Print the original DataFrame> print>(>'Original DataFrame:'>)> print>(df)> # Select Rows Between 'Row_2' and 'Row_4'> selected_rows>=> df.loc[>'Row_2'>:>'Row_4'>]> print>(>'
Selected Rows:'>)> print>(selected_rows)> # Select Columns 'B' through 'D'> selected_columns>=> df.loc[:,>'B'>:>'D'>]> print>(>'
Selected Columns:'>)> print>(selected_columns)> |
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Izhod
Original DataFrame: A B C D Row_1 12.0 7 20 14.0 Row_2 4.0 2 16 3.0 Row_3 5.0 54 11 NaN Row_4 NaN 3 3 2.0 Row_5 1.0 NaN 8 6.0 Selected Rows: A B C D Row_2 4 2 16 3.0 Row_3 5 54 11 NaN Row_4 NaN 3 3 2.0 Selected Columns: B C D Row_1 7 20 14.0 Row_2 2 16 3.0 Row_3 54 11 NaN Row_4 3 3 2.0 Row_5 NaN 8 6.0>
4. primer: izberite nadomestne vrstice ali stolpce
V tem primeru ustvarimo pandas DataFrame z imenom 'df', nastavimo indekse vrstic po meri in nato uporabimoiloc>dostopnik za izbiro nadomestnih vrstic (vsaka druga vrstica) in nadomestnih stolpcev (vsak drugi stolpec). Nastali izbori so natisnjeni in prikazujejo uporabo indeksiranja na podlagi celih števil ziloc>.
Python3
# importing pandas as pd> import> pandas as pd> # Creating the DataFrame> df>=> pd.DataFrame({>'A'>: [>12>,>4>,>5>,>None>,>1>],> >'B'>: [>7>,>2>,>54>,>3>,>None>],> >'C'>: [>20>,>16>,>11>,>3>,>8>],> >'D'>: [>14>,>3>,>None>,>2>,>6>]})> # Create the index> index_>=> [>'Row_1'>,>'Row_2'>,>'Row_3'>,>'Row_4'>,>'Row_5'>]> # Set the index> df.index>=> index_> # Print the original DataFrame> print>(>'Original DataFrame:'>)> print>(df)> # Select Alternate Rows> alternate_rows>=> df.iloc[::>2>]> print>(>'
Alternate Rows:'>)> print>(alternate_rows)> # Select Alternate Columns> alternate_columns>=> df.iloc[:, ::>2>]> print>(>'
Alternate Columns:'>)> print>(alternate_columns)> |
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Izhod
Original DataFrame: A B C D Row_1 12.0 7 20 14.0 Row_2 4.0 2 16 3.0 Row_3 5.0 54 11 NaN Row_4 NaN 3 3 2.0 Row_5 1.0 NaN 8 6.0 Alternate Rows: A B C D Row_1 12.0 7 20 14.0 Row_3 5.0 54 11 NaN Row_5 1.0 NaN 8 6.0 Alternate Columns: A C Row_1 12.0 20 Row_2 4.0 16 Row_3 5.0 11 Row_4 NaN 3 Row_5 1.0 8>
Primer 5: Uporaba pogojev s Pandas loc
V tem primeru ustvarjamo pandas DataFrame z imenom 'df', nastavljamo indekse vrstic po meri in uporabljamoloc>dostopnik za izbiro vrstic glede na pogoje. Prikazuje izbiranje vrstic, kjer ima stolpec 'A' vrednosti, večje od 5, in izbiranje vrstic, kjer stolpec 'B' ni nič. Dobljeni izbori se nato natisnejo in prikazujejo uporabo pogojnega filtriranja zloc>.
Python3
# importing pandas as pd> import> pandas as pd> # Creating the DataFrame> df>=> pd.DataFrame({>'A'>: [>12>,>4>,>5>,>None>,>1>],> >'B'>: [>7>,>2>,>54>,>3>,>None>],> >'C'>: [>20>,>16>,>11>,>3>,>8>],> >'D'>: [>14>,>3>,>None>,>2>,>6>]})> # Create the index> index_>=> [>'Row_1'>,>'Row_2'>,>'Row_3'>,>'Row_4'>,>'Row_5'>]> # Set the index> df.index>=> index_> # Print the original DataFrame> print>(>'Original DataFrame:'>)> print>(df)> # Using Conditions with loc> # Example: Select rows where column 'A' is greater than 5> selected_rows>=> df.loc[df[>'A'>]>>5>]> print>(>'
Rows where column 'A' is greater than 5:'>)> print>(selected_rows)> # Example: Select rows where column 'B' is not null> non_null_rows>=> df.loc[df[>'B'>].notnull()]> print>(>'
Rows where column 'B' is not null:'>)> print>(non_null_rows)> |
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Izhod
Original DataFrame: A B C D Row_1 12.0 7 20 14.0 Row_2 4.0 2 16 3.0 Row_3 5.0 54 11 NaN Row_4 NaN 3 3 2.0 Row_5 1.0 NaN 8 6.0 Rows where column 'A' is greater than 5: A B C D Row_1 12.0 7 20 14.0 Row_3 5.0 54 11 NaN Rows where column 'B' is not null: A B C D Row_1 12.0 7 20 14.0 Row_2 4.0 2 16 3.0 Row_3 5.0 54 11 NaN Row_4 NaN 3 3 2.0>