We can pass index_col parameter to use that column as index instead of the on that Pandas adds. Names=,Īs you can see from our data snapshot, our csv file comes with an index. Be sure to add header=0 if csv file has an header, else header will be included as a row # read CSV with custom headerĭf = pd.read_csv( './data/the_office/the_office_lines.csv', We can also ignore headers in the csv file and give our own custom column names by passing names parameter to the function. # read CSV with different headerĭf = pd.read_csv( './data/the_office/the_office_lines.csv', header= 1) We can change that by passing the header parameter. Standard encodings supported by Python can be found here # read CSV with encodingĭf = pd.read_csv( './data/the_office/the_office_lines.csv', encoding= 'utf-8')īy default first row is treated as column header or column names for the data frame. We can pass encoding parameter to open files with different encodings. # print the first 5 rows of the dataframe # read CSV file df = pd.read_csv( './data/the_office/the_office_lines.csv') Reading CSV files is the easiest we can just use the read_csv method. We going to cover reading csv, tsv, json and excel files. This can be done by querying databases, making server requests and most commonly by reading data files. The first step into any Exploratory Data Analysis is to read data into the memory.
All the data files using in this blog are here and the Jupyter Notebook is here. I've converted CSV to other formats to cover different scenarios. We are going to use The Office data set from Kaggle as an example. This article assumes you have basic knowledge of Python and have a working dev environment setup. How to identify bad data using Pandas? - TBD.This is going to be a multi-part series starting with, following four articles In this series we are going to look at some basic methods to get started with Exploratory Data Analysis with Pandas. Its commonly used for data manipulation and analysis.
Pandas is an open source library written for Python.
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