Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Pandas DataFrame.ix[ ] is both Label and Integer based slicing technique. Besides pure label based and integer based, Pandas provides a hybrid method for selections and subsetting the object using the ix[] operator. ix[] is the most general indexer and will support any of the inputs in loc[] and 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 rowsReturns: Data frame or Series depending on parameters
# importing pandas package import pandas as geek # making data frame from csv file # Integer slicingprint("Slicing only rows(till index 4):")x1 = data.ix[:4, ]print(x1, "\n") print("Slicing rows and columns(rows=4, col 1-4, excluding 4):")x2 = data.ix[:4, 1:4]print(x2) |
Output :
Code #2:
# importing pandas package import pandas as geek # making data frame from csv filedata = geek.read_csv("nba.csv") # Index slicing on Height columnprint("After index slicing:")x1 = data.ix[10:20, 'Height']print(x1, "\n") # Index slicing on Salary columnx2 = data.ix[10:20, 'Salary']print(x2) |
Output:
Code #3:
# importing pandas and numpyimport pandas as pdimport numpy as np df = pd.DataFrame(np.random.randn(10, 4), columns = ['A', 'B', 'C', 'D']) print("Original DataFrame: \n" , df) # Integer slicingprint("\n Slicing only rows:")print("--------------------------")x1 = df.ix[:4, ]print(x1) print("\n Slicing rows and columns:")print("----------------------------")x2 = df.ix[:4, 1:3]print(x2) |
Output :
Code #4:
# importing pandas and numpyimport pandas as pdimport numpy as np df = pd.DataFrame(np.random.randn(10, 4), columns = ['A', 'B', 'C', 'D']) print("Original DataFrame: \n" , df) # Integer slicing (printing all the rows of column 'A')print("\n After index slicing (On 'A'):")print("--------------------------")x = df.ix[:, 'A'] print(x) |
Output :
