pandas-selector 1.2.1

Simple, composable selectors for loc[], iloc[], assign() and others.

Pandas Selector

Access the calling pandas data frame in loc[], iloc[], assign() and other methods with DF to write better chains of data frame operations, e.g.:

df = (df
      # Select all rows with column "x" < 2
      .loc[DF["x"] < 2]
      .assign(
          # Shift "x" by its minimum.
          y = DF["x"] - DF["x"].min(),
          # Clip "x" to it's central 50% window. Note how DF is used
          # in the argument to `clip()`.
          z = DF["x"].clip(
              lower=DF["x"].quantile(0.25),
              upper=DF["x"].quantile(0.75)
          ),
      )
     )

Overview

• Motivation: Make chaining Pandas operations easier and bring functionality to Pandas similar to Spark’s col() function or referencing columns in R’s dplyr.

• Install from PyPI with pip install pandas-selector. Pandas versions 1.0+ (^1.0) are supported.

• Documentation can be found at readthedocs.

• Source code can be obtained from GitHub.

Example: Create new column and filter

Instead of writing “traditional” Pandas like this:

df_in = pd.DataFrame({"x": range(5)})
df = df_in.assign(y = df_in["x"] // 2)
df = df.loc[df["y"] <= 1]
df
#    x  y
# 0  0  0
# 1  1  0
# 2  2  1
# 3  3  1

One can write:

from pandas_selector import DF
df = (df_in
      .assign(y = DF["x"] // 2)
      .loc[DF["y"] <= 1]
     )

This is especially handy when re-iterating on data frame manipulations interactively, e.g. in a notebook (just imagine you have to rename df to df_out).

But you can access all methods and attributes of the data frame from the context:

df = pd.DataFrame({
    "X": range(5),
    "y": ["1", "a", "c", "D", "e"],
})
df.loc[DF["y"]str.isupper() | DF["y"]str.isnumeric()]
#    X  y
# 0  0  1
# 3  3  D
df.loc[:, DF.columns.str.isupper()]
#    X
# 0  0
# 1  1
# 2  2
# 3  3
# 4  4

You can even use DF in the arguments to methods:

df = pd.DataFrame({
    "x": range(5),
    "y": range(2, 7),
})
df.assign(z = DF['x'].clip(lower=2.2, upper=DF['y'].median()))
#    x  y    z
# 0  0  2  2.2
# 1  1  3  2.2
# 2  2  4  2.2
# 3  3  5  3.0
# 4  4  6  4.0

When working with ~pd.Series the S object exists. It can be used similar to DF:

s = pd.Series(range(5))
s[s < 3]
# 0    0
# 1    1
# 2    2
# dtype: int64

Similar projects for pandas

• siuba

  • (+) active

  • (-) new API to learn

• pandas-ply

  • (-) stale(?), last change 6 years ago

  • (-) new API to learn

  • (-) Symbol / pandas_ply.X works only with ply_* functions

• pandas-select

  • (+) no explicite df necessary

  • (-) new API to learn

• pandas-selectable

  • (+) simple select accessor

  • (-) usage inside chains clumsy (needs explicite df):

    ((df
      .select.A == 'a')
      .select.B == 'b'
    )

  • (-) hard-coded str, dt accessor methods

  • (?) composable?

Development

Development is containerized with [Docker](https://www.docker.com/) to separte from host systems and improve reproducability. No other prerequisites are needed on the host system.

Recommendation for Windows users: install WSL 2 (tested on Ubuntu 20.04), and for containerized workflows, Docker Desktop for Windows.

The common tasks are collected in Makefile (See make help for a complete list):

• Run the unit tests: make test or make watch for continuously running tests on code-changes.

• Build the documentation: make docs

• TODO: Update the poetry.lock file: make lock

• Add a dependency:

  1. Start a shell in a new container.

  2. Add dependency with poetry add in the running container. This will update poetry.lock automatically:

     # 1. On the host system
     % make shell
     # 2. In the container instance:
     I have no name!@7d0e85b3a303:/app$ poetry add --dev --lock falcon

• Build the development image make devimage (Note: This should be done automatically for the targets.)