Data Preprocessing with Pandas
IMPORT DATA
CSV
EXCEL
SCRAPE DATA
PREPARE DATA
CONVERTING
CLEANING NANS
CLEANING STRINGS
SELECTING & RENAMING
SAMPLING
FILTERING
TRANSFORM
SUMMARISE
JOIN
MERGE
UNION
FEATURE ENGINEERING
WINDOW & LAG FEATURES
STRINGS
DATES
STATISTICS & FORMULAS
Parsing a Date Column When Reading a CSV with Pandas
Python
Import and Export
To parse columns as a datetime data type when reading a csv into a Pandas dataframe, simply pass a list of column names that contain dates into the parse_dates parameter.
In the example below we parse the columns order_date and shipping_date as datetime data types.
1| import pandas as pd 2| 3| date_columns = ['order_date', 'shipping_date'] 4| df = pd.read_csv('sales.csv', parse_dates=date_columns
2
Creating a Pandas DataFrame from a Python Generator
Python
Import and Export
2
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2
Reading CSV Files from Amazon S3 into Pandas Dataframes
Python
Import and Export
1
Sklearn Iris Dataset - Load The Iris Dataset & Split Into Features & Target
Python
Import and Export
1
Using Selectors in Beautiful Soup to Scrape Using Classes & IDs
Python
Import and Export
1
1
1
Find the Nth Tag On a WebPage with Beautiful Soup
Python
Import and Export
1