Quick Start¶

Adding Summaries¶

PrettyPandas supports many built in summary functions, as well as providing the ability to create your own summaries. Summary functions can be applied over a DataFrame’s rows or columns, or both.

The builtin summary methods are:

If you wanted to add a grand total to the bottom of your table the code is simple:

PrettyPandas(df).total()

Or additionally if you want to use Pandas fluent API:

df.pipe(PrettyPandas).total()

PrettyPandas follows a fluent API so you can chain multiple summaries easily:

df.pipe(PrettyPandas).total().average()

The axis parameter specifies which numpy style axis to apply a summary on — 0 for columns, 1 for rows, and None for both.

PrettyPandas(df).total(axis=1)

You can even mix and match summaries applied to different axis.

Creating a Custom Summary¶

The summary method creates a custom summary from a function which takes an array-like structure as a list.

def count_greater_than_zero(column):
    return (column > 0).sum()

PrettyPandas(df).summary(count_greater_than_zero, title="> 0")

Converting Back to Pandas DataFrame¶

`.to_frame()`¶

After adding summary rows or columns you can get a DataFrame with your changes applied by calling the ._to_frame.

For example the following code would add a total to your DataFrame and return it back to a Pandas native DataFrame.

(
    df
    .pipe(PrettyPandas)
    .total(axis=1)
    .to_frame()
)

`.style`¶

The .style property allows you to drop right into the Pandas Style API. This code would allow you to compute a summary, format the table using percentages, and apply a backgrouned gradient to a table:

(
    df.pipe(PrettyPandas)
    .as_percent(precision=0)
    .median()
    .style
    .background_gradient()
)

Formatting Numbers¶

Most reports use at least some units of measurement. PrettyPandas currently supports percentages, money, and a more general unit method.

The as_unit method takes a positional unit argument which indicates the string representing the unit to be used and a location argument to specify whether the unit should be a prefix or suffix to the value.

The as_currency and as_percent methods are localized to use whatever units your Python distribution thinks are best for you. If you aren’t getting the correct units use the set_locale method to specify your locale.

If you need to use a different currency, just pass it to currency='...' to change it.

The as_money method takes optional currency and location arguments which work just like the as_unit method. By default the currency is in dollars.

Note

Python 2 doesn’t support unicode literals by default. You can use unicode literals (e.g. u'€') or import the unicode literal behaviour from Python 3:

from __future__ import unicode_literals

Formatting Columns¶

By default the formatting methods apply to the entire dataframe. When you need to format just a few columns you can use the subset argument to specify a single column, or multiple columns.

PrettyPandas(df).as_percent(subset='A')  # Format just column A

PrettyPandas(df).as_percent(subset=['A', 'B'])  # Format columns A and B

Formatting Rows and Complex Formatting¶

Formatting rows is more complicated than formatting columns. The subset argument needs to take in a pandas.Index to specify the row.

# Format the row with row-index 3
PrettyPandas(df).as_percent(subset=pd.IndexSlice[3,:], precision=2)

For multi-index dataframes subsetting is more complicated. You will need to use multiple pandas.IndexSlice objects to get the correct rows.

The following example shows how to select rows in a multi-index:

first_row_idx = pd.IndexSlice[0, :]
second_row_idx = pd.IndexSlice[1, :]

(
    df.pipe(PrettyPandas)
    .as_currency(subset=first_row_idx)
    .as_percent(subset=second_row_idx)
    .total(axis=1)
)

For more info on Pandas indexing, read Pandas Indexing and Pandas Advanced Indexing.