DataCamp - Cleaning Data in Python | Joanna

Datacamp course notes on data cleaning.

Datacamp course notes on data cleaning. Common Data Problems Inconsistent column names (capitalization) Missing data Outliers Duplicate rows (can bias analysis and should be dropped) Untidy Need to process between colums Column types can signal unexpected data values 1 2 3 4 5 6 import pandas as pd df.head() df.tail() df.columns #returns column names df.shape #returns dimension df.info() #additional info about the df Exploratory Data Analysis Frequency count value_counts() is a method we used here to count the number of unique values in each column in descending order 1 2 3 4 5 6 df.info() #kn

related reading

• 12 Tidy data | Python for Data Sciencebyuidatascience.github.io
• Pandas Dataframes: OxRSE Trainingtrain.oxrse.uk
• Intro to data structures — pandas 3.0.3 documentationpandas.pydata.org
• python - pandas apply function that returns multiple values to rows in pandas dataframe - Stack Overflowstackoverflow.com
• Stop aggregating away the signal in your data - Stack Overflowstackoverflow.blog
• Getting started — pandas 3.0.3 documentationpandas.pydata.org
• Data vis do’s & don’ts | Datawrapper Blogdatawrapper.de
• Synthetic data generation (Part 1)cookbook.openai.com
• Python Reference | Data 8data8.org
• pandas.DataFrame — pandas 3.0.5 documentationpandas.pydata.org
• 2. Exploratory analysis - Splinkmoj-analytical-services.github.io
• styleguide | Style guides for Google-originated open-source projectsgoogle.github.io