Python basics: from a variable to a checked pandas report
I follow one complete example from reading orders to checking inputs and explaining a grouped report without hiding missing amounts.
Knowledge base / Python
Python: pandas and NumPy with examples
The most useful pandas and NumPy methods: reading data, cleaning, joining tables, grouping and time series.
100 entries
The Python entries focus on pandas, the library for working with tables: reading files, cleaning, joining, grouping and time series. Separate groups cover NumPy, which pandas is built on.
pandas changes faster than Excel or SQL. Some methods that used to be standard have been removed. DataFrame.append is one example; pandas.concat replaces it. If code from a tutorial raises a FutureWarning or an error, check the library version with pd.__version__ and compare the behaviour with the documentation for that version.
If you are new to Python, start with Python basics. It goes from a variable to a small pandas report, and the entries in this section help you expand each step.
pandas: reading and writing 8
pandas: structure and inspection 11
pandas: cleaning and types 14
- pd.isna()
- pd.notna()
- DataFrame.dropna()
- DataFrame.fillna() (updated entry)
- DataFrame.interpolate()
- DataFrame.replace()
- DataFrame.astype()
- pd.to_numeric() (updated entry)
- pd.to_datetime()
- DataFrame.duplicated()
- DataFrame.drop_duplicates() (updated entry)
- pd.unique() (updated entry)
- DataFrame.nunique() (updated entry)
- Series.value_counts()
pandas: selecting, filtering and sorting 11
pandas: combining and reshaping 12
pandas: grouping and calculations 10
pandas: time series and windows 4
NumPy: creating arrays 8
NumPy: shape and joining 10
NumPy: selection, conditions and maths 12
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