Handling Missing Data (Data Cleaning) - Pandas | Python ~ Episode 6
Hey Coders! As much as we dream of a perfect world, when it comes to raw data found out in the wild, it will most likely be patchy. Missing data is one of the most prevalent causes of data cleaning. Sometimes, non-existent data is OK, but for those other times, pandas provides a suite of methods and techniques to resolve missing values. Some examples are dropping the row altogether, or a more gentle approach, of filling in those missing vales with another value. One example could be filling in missing numbers within a column of numbers with the average of the numbers already there. That way, recalculating the average won't change the outcome.
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https://github.com/davidtheweiss/pand...
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🧑💻 Pandas Documentation: https://pandas.pydata.org/docs/refere...
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🕒 Timestamps
0:00 Intro
1:13 Importing pandas and displaying our data
1:44 Creating artificial missing data
2:40 isna() to reveal missing values in your dataframe
5:29 notna() to filter out null values in your dataframe
6:45 dropna() removes rows/columns with missing values
12:06 fillna() to replace null values with data
15:45 interpolate() to fill in data gaps by guessing trend
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