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Data preparation — Machine Learning Zoomcamp Module 3

This session covered data obtention and some procedures of data preparation.

mlz-module-03

Notes

This session covered data obtention and some procedures of data preparation.

Commands, functions, and methods:

!wget - Linux shell command for downloading data
pd.read.csv() - read csv files
df.head() - take a look of the dataframe
df.head().T - take a look of the transposed dataframe
df.columns - retrieve column names of a dataframe
df.columns.str.lower() - lowercase all the letters in the columns names of a dataframe
df.columns.str.replace(' ', '_') - replace the space separator in the columns names of a dataframe
df.dtypes - retrieve data types of all series
df.index - retrieve indices of a dataframe
pd.to_numeric() - convert a series values to numerical values. The errors='coerce' argument allows making the transformation despite some encountered errors.
df.fillna() - replace NAs with some value
(df.x == "yes").astype(int) - convert x series of yes-no values to numerical values.

The entire code of this project is available in this jupyter notebook.

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