Pandas is the most popular python library that is used for data analysis. It provides highly optimized performance with back-end source code is purely written in C or Python.
Way to analyse pandas in python:
Pandas DataFrame is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). A Data frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns. Pandas DataFrame consists of three principal components, the data, rows, and columns.
Here we understand it easily by below some examples:
Importing csv file:
We import Contrib_data.csv by using read_csv().
Select column from csv file
Selecting row from csv file:
Selecting a single row from the csv file:
Data framing using loc and iloc:
Dataframe using iloc:
data.iloc # first row of data frame (Aleshia Tomkiewicz)
- Note a Series data type output.
data.iloc # second row of data frame (Evan Zigomalas)
data.iloc[-1] # last row of data frame (Mi Richan)# Columns:
data.iloc[:,0] # first column of data frame (first_name)
data.iloc[:,1] # second column of data frame (last_name)
data.iloc[:,-1] # last column of data frame (id)
Multiple row and column selections using iloc and DataFrame
data.iloc[0:5] # first five rows of dataframe
data.iloc[:, 0:2] # first two columns of data frame with all rows
data.iloc[[0,3,6,24], [0,5,6]] # 1st, 4th, 7th, 25th row + 1st 6th 7th columns.
data.iloc[0:5, 5:8] # first 5 rows and 5th, 6th, 7th columns of data frame
(county -> phone1).
Dataframe using loc:
The Pandas loc indexer can be used with DataFrames for two different use cases:
Selecting row by boolean
Selecting row by label
Select row by label:
Find all CAND_NAME whose name is WARREN,ELIZABETH
In next tutorial we discuss some complex example which is very useful for any Data Analyst.
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