Limited rows selection with given column in Pandas | Python
Last Updated : 24 Oct, 2019
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Methods in Pandas like Python3 Python3 Python3
iloc[], iat[]are generally used to select the data from a given dataframe. In this article, we will learn how to select the limited rows with given columns with the help of these methods. Example 1: Select two columns# Import pandas package
import pandas as pd
# Define a dictionary containing employee data
data = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'],
'Age':[27, 24, 22, 32],
'Address':['Delhi', 'Kanpur', 'Allahabad', 'Kannauj'],
'Qualification':['Msc', 'MA', 'MCA', 'Phd']}
# Convert the dictionary into DataFrame
df = pd.DataFrame(data)
# select three rows and two columns
print(df.loc[1:3, ['Name', 'Qualification']])
Name Qualification 1 Princi MA 2 Gaurav MCA 3 Anuj PhdExample 2: First filtering rows and selecting columns by label format and then Select all columns.
# Import pandas package
import pandas as pd
# Define a dictionary containing employee data
data = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'],
'Age':[27, 24, 22, 32],
'Address':['Delhi', 'Kanpur', 'Allahabad', 'Kannauj'],
'Qualification':['Msc', 'MA', 'MCA', 'Phd']
}
# Convert the dictionary into DataFrame
df = pd.DataFrame(data)
# .loc DataFrame method
# filtering rows and selecting columns by label format
# df.loc[rows, columns]
# row 1, all columns
print(df.loc[0, :] )
Address Delhi Age 27 Name Jai Qualification Msc Name: 0, dtype: objectExample 3: Select all or some columns, one to another using .iloc.
# Import pandas package
import pandas as pd
# Define a dictionary containing employee data
data = {'Name':['Jai', 'Princi', 'Gaurav', 'Anuj'],
'Age':[27, 24, 22, 32],
'Address':['Delhi', 'Kanpur', 'Allahabad', 'Kannauj'],
'Qualification':['Msc', 'MA', 'MCA', 'Phd']}
# Convert the dictionary into DataFrame
df = pd.DataFrame(data)
# iloc[row slicing, column slicing]
print(df.iloc [0:2, 1:3] )
Age Name 0 27 Jai 1 24 Princi
