Pandas DataFrame.ix [] — it is a label-based slicing technique and DataFrame.ix [] . In addition to pure label and integer based data, Pandas provides a hybrid method for selecting and subsetting an object using the ix [] operator. ix [] is the most general indexer and will support any input in iloc [] .
Syntax: DataFrame.ix []
Parameters:
Index Position : Index position of rows in integer or list of integer.
Index label: String or list of string of index label of rows
Returns: Data frame or Series depending on parameters
Code # 1:
| # pandas package import import pandas as geek
# create a data frame from a CSV file data = geek.read_csv ( " https://media.python.engineering/wp-content/uploads/nba.csv " )
# Integer slicing print ( "Slicing only rows (till index 4):" ) x1 = data.ix [: 4 ,] print (x1, " " ) print ( "Slicing rows and columns (rows = 4, col 1-4, excluding 4): " ) x2 = data.ix [: 4 , 1 : 4 ] print (x2) |
Output:
Code no. 2:
| # pandas package import import pandas as geek
# create data frame from CSV file data = geek.read_csv ( "nba.csv" )
# Index slicing by column Height print ( "After index slicing:" ) x1 = data.ix [ 10 : 20 , ’Height’ ] print (x1, " " )
# Column slicing index Salary x2 = data.ix [ 10 : 20 , ’Salary’ ] print (x2) |
Output:
Code # 3:
| # pandas and numpy imports import pandas as pd import numpy as np df = pd.DataFrame (np.random.randn ( 10 , 4 ), columns = [ ’A’ , ’B’ , ’ C’ , ’D’ ]) print ( "Original DataFrame:" , df)
# Integer slicing print ( "Slicing only rows:" ) print ( " --------------------- ----- " ) x1 = df.ix [: 4 ,] print (x1) print ( "Slicing rows and columns: " ) print ( "----------------------------" ) x2 = df.ix [: 4 , 1 : 3 ] print (x2) |
Output:
Code # 4:
| # pandas and numpy imports import pandas as pd import numpy as np df = pd.DataFrame (np.random.randn ( 10 , 4 ), columns = [ ’A’ , ’ B’ , ’C’ , ’ D’ ]) print ( "Original DataFrame:" , df)
# Integer slicing (prints all rows in a column & # 39; A & # 39;) print ( " After index slicing (On ’A’):" ) print ( "---------- ---------------- " ) x = df.ix [:, ’A’ ] print (x) |
Output:
