pandacell 2020.9.19

IPython wrapper to more easily manipulate Pandas dataframes.

pandacell

Author: Eirik B. Stavestrand

Introduces a %df (or %%df) magic which can be used in Jupyter notebooks and the IPython console.

The magic executes the contents of a cell on a Pandas DataFrame.

Description

Pandas is great and all, but writing Pandas code can be tedious. For example when simply making summing two columns:

    In [1]: df["a"] + df["b"]

It might not look like such a big deal, but all those brackets and quotation marks add up. Using pandacell, the above syntax can be written as:

    In [2]: %df a + b

Under the hoods, this is accomplished simply by passing the cell contents as a string to Pandas' df.eval function. This isn't very complex, but it does provide a fair deal of functionality and adds a whole lot of readability.

If you wish to store the results to a new column, use regular assignment along with the -i (or --inplace) flag:

    In [3]: %df -i c = a + b

It also works with multiple assignments:

    In [4]: %%df -i
       ...: c = a + b
       ...: f = c - a

You can use Pandas' various accessors and series method calls:

    In [5]: %%df -i
       ...: name_upper = name.str.upper()
       ...: yr = timestamp.dt.year
       ...: lower_cased = species.where(cond=species.str[0].str.islower(), other=None)

Since variable names are assumed to be columns in the dataframe, regular variables in the local/global namespace can be accessed by prefixing with @

    In [6]: a = 1
       ...: %df a = @a + 1

    In [7]: def myfunc(row):
        ...:     return row + 43
        ...: %df b = a.apply(@myfunc)

By default, pandacell operates on any dataframe named df. This can be overridden with the -n (or --name) flag:

    In [8]: %df -n=df_in c = a + b

You can also print subset a dataframe with the -q (or --query) flag:

    In [9]: %df -q species == "setosa"
    Out[9]:
        sepal_length  sepal_width  petal_length  petal_width species  a
    0            5.1          3.5           1.4          0.2  setosa  0
    1            4.9          3.0           1.4          0.2  setosa  0


    In [10]: %df -q species.isna() #check for missing values
    Out[10]:
    Empty DataFrame
    Columns: [sepal_length, sepal_width, petal_length, petal_width, species]
    Index: []

This can be combined with the -i flag to subset the dataframe in-place:

    In [10]: %df -q -i species == "setosa"

Pandacell even supports comments

    In [11]: %%df -i
       ...: # Line comment
       ...: c = a + b # Comment at end of line

Inspired by

https://github.com/catherinedevlin/ipython-sql

Development

https://github.com/eirki/pandacell