extra_ds_tools.plots.format

Module Contents

Functions

add_counts_to_yticks(→ Tuple[matplotlib.pyplot.Figure, ...)

Adds count statistics to y ticks on a matplotlib Figure.

add_counts_to_xticks(→ Tuple[matplotlib.pyplot.Figure, ...)

Adds count statistics to x ticks on a matplotlib Figure.
extra_ds_tools.plots.format.add_counts_to_yticks(fig: matplotlib.pyplot.Figure, ax: matplotlib.pyplot.Axes, df: pandas.DataFrame, x_col: str, y_col: str, dropna: bool = False) → Tuple[matplotlib.pyplot.Figure, matplotlib.pyplot.Axes]

Adds count statistics to y ticks on a matplotlib Figure.

Parameters

  • fig (plt.Figure) – The matplotlib Figure.

  • ax (plt.Axes) – The matplotlib Axes to add the count information to.

  • df (pd.DataFrame) – The pandas DataFrame with the data of the plot.

  • x_col (str) – Name of the column with the values on the x-axis.

  • y_col (str) – Name of the column with the values on the y-axis.

  • dropna (bool, optional) – Whether to drop count statistics about na values, by default False

Returns

Returns the Figure and Axes with the added count statistics.

Return type

Tuple[plt.Figure, plt.Axes]

Examples

>>> from numpy.random import default_rng
>>> import matplotlib.pyplot as plt
>>> import seaborn as sns
>>> import pandas as pd
>>> import numpy as np
# generate data
>>> rng = default_rng(42)
>>> cats = ['Cheetah', 'Leopard', 'Puma']
>>> cats = rng.choice(cats, size=1000)
>>> cats = np.append(cats, [None]*102)
>>> weights = rng.integers(25, 100, size=1000)
>>> weights = np.append(weights, [np.nan]*100)
>>> weights = np.append(weights, np.array([125,135]))
>>> rng.shuffle(cats)
>>> rng.shuffle(weights)
>>> df = pd.DataFrame({'cats': cats, 'weights': weights})

>>> fig, ax = plt.subplots()
>>> sns.boxplot(data=df, x='weights', y='cats', ax=ax)
>>> fig, ax = add_counts_to_ticks(fig, ax, df, 'weights', 'cats')
>>> fig
../../../../_images/add_counts_to_yticks.png

Drop na statistics:

>>> fig, ax = plt.subplots()
>>> sns.boxplot(data=df, x='weights', y='cats', ax=ax)
>>> fig, ax = add_counts_to_yticks(fig, ax, df, 'weights', 'cats', dropna=True)
>>> fig
../../../../_images/add_counts_to_yticks_dropna.png
extra_ds_tools.plots.format.add_counts_to_xticks(fig: matplotlib.pyplot.Figure, ax: matplotlib.pyplot.Axes, df: pandas.DataFrame, x_col: str, y_col: str, dropna: bool = False) → Tuple[matplotlib.pyplot.Figure, matplotlib.pyplot.Axes]

Adds count statistics to x ticks on a matplotlib Figure.

Parameters

  • fig (plt.Figure) – The matplotlib Figure.

  • ax (plt.Axes) – The matplotlib Axes to add the count information to.

  • df (pd.DataFrame) – The pandas DataFrame with the data of the plot.

  • x_col (str) – Name of the column with the values on the x-axis.

  • y_col (str) – Name of the column with the values on the y-axis.

  • dropna (bool, optional) – Whether to drop count statistics about na values, by default False

Returns

Returns the Figure and Axes with the added count statistics.

Return type

Tuple[plt.Figure, plt.Axes]

Examples

>>> from numpy.random import default_rng
>>> import matplotlib.pyplot as plt
>>> import seaborn as sns
>>> import pandas as pd
>>> import numpy as np
# generate data
>>> rng = default_rng(42)
>>> cats = ['Cheetah', 'Leopard', 'Puma']
>>> cats = rng.choice(cats, size=1000)
>>> cats = np.append(cats, [None]*102)
>>> weights = rng.integers(25, 100, size=1000)
>>> weights = np.append(weights, [np.nan]*100)
>>> weights = np.append(weights, np.array([125,135]))
>>> rng.shuffle(cats)
>>> rng.shuffle(weights)
>>> df = pd.DataFrame({'cats': cats, 'weights': weights})

>>> fig, ax = plt.subplots()
>>> sns.boxplot(data=df, x='cats', y='weights', ax=ax)
>>> fig, ax = add_counts_to_xticks(fig, ax, df, 'cats', 'weights')
>>> fig
../../../../_images/add_counts_to_xticks.png

Drop na statistics:

>>> fig, ax = plt.subplots()
>>> sns.boxplot(data=df, x='weights', y='cats', ax=ax)
>>> fig, ax = add_counts_to_xticks(fig, ax, df, 'weights', 'cats', dropna=True)
>>> fig
../../../../_images/add_counts_to_xticks_dropna.png