python-kplot 0.0.2

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kplot

kplot is a Python package about plotting. The sections below introduce the package and show quick-start examples from each exported module.

Installation

pip install -U python-kplot

Quick start

The examples below follow the notebooks under nbs/ in order. Each function example lives in its own cell and starts with a short comment derived from the function docstring.

01 utils

from kplot.utils import set_sns, save_svg, save_pdf, save_show, get_color_dict, get_plt_color, get_hue_big, add_stats

import seaborn as sns
from matplotlib import pyplot as plt

# Set up the objects used by the examples below.
df = sns.load_dataset('tips')
df.shape

(244, 7)

# Set seaborn defaults for notebook display and saved figures.
set_sns(dpi=50)

# Save the current matplotlib figure as SVG with editable text.
plt.figure()
plt.plot([0, 1], [0, 1])
# save_svg(Path('nbs') / '_tmp_utils.svg')

# Save the current matplotlib figure as PDF with TrueType fonts.
plt.figure()
plt.plot([0, 1], [1, 0])
# save_pdf(Path('nbs') / '_tmp_utils.pdf')

# Show the current figure or save it, then close open figures.
plt.figure()
plt.plot([0, 1], [0.5, 0.5])
# save_show(path=Path('nbs') / '_tmp_utils_show.png')

# Assign colors to labels while tolerating duplicate category names.
get_color_dict(['A', 'B', 'C'], palette='Set2')

{'A': (0.4, 0.7607843137254902, 0.6470588235294118),
 'B': (0.9882352941176471, 0.5529411764705883, 0.3843137254901961),
 'C': (0.5529411764705883, 0.6274509803921569, 0.796078431372549)}

# Return colors in plotting order for a dict, list, or named palette.
get_plt_color('Set2', ['a', 'b'])

# Filter a hue column down to categories that meet a count threshold.
# get_hue_big(df, 'day', cnt_thr=40).tolist()

# If `value` is str: compare between groups (x=group, y=value) If `value` is list/tuple: compare among values within each group (x=group, hue='variable')
fig, ax = plt.subplots(figsize=(5, 4))
sns.boxplot(data=df, x='sex', y='total_bill', ax=ax)
add_stats(ax, df, value='total_bill', group='sex')

02 scatter

from kplot.scatter import reduce_feature, plot_2d, plot_cluster, plot_rel

import seaborn as sns

# Set up the objects used by the examples below.
df = sns.load_dataset('penguins').dropna().reset_index(drop=True)
df2 = df[['bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g']]
print(df.shape)
print(df2.shape)

(333, 7)
(333, 4)

# Reduce a feature matrix to a lower-dimensional embedding dataframe.
reduce_feature(df2, method='pca', n=2)

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </style>

	PCA1	PCA2
0	-457.325073	-13.351587
1	-407.252205	-9.179113
2	-957.044676	8.160444
3	-757.115802	1.867653
4	-557.177302	-3.389158
...	...	...
328	718.068699	2.338199
329	643.090909	4.280699
330	1543.098355	-2.232010
331	992.994900	-4.605154
332	1193.002584	-5.417312

333 rows × 2 columns

# Plot the first two columns of an embedding dataframe.
df2 = reduce_feature(df[['bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g']], method='pca', n=2)
df2['species'] = df['species'].values
plot_2d(df2, hue='species', legend=True)

# Reduce features and immediately plot the first two embedding dimensions.
plot_cluster(df[['bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g', 'species']], method='pca', hue='species', legend=True)

# Plot a pairwise relationship with an optional correlation annotation.
df2 = df[['bill_length_mm', 'flipper_length_mm', 'species']].head(12).copy()
df2.index = [f'pt{i}' for i in range(len(df2))]
plot_rel(df2, x='bill_length_mm', y='flipper_length_mm', hue='species', index_list=['pt0', 'pt11'])

03 bar

from kplot.bar import plot_hist, plot_count, plot_bar, plot_group_bar, plot_stacked, plot_violin, plot_box, plot_pie, plot_cnt, calculate_pct, plot_composition

import seaborn as sns

# Set up the objects used by the examples below.
df = sns.load_dataset('tips').dropna()
df.shape

(244, 7)

# Plot a histogram with a KDE overlay and polygon bins.
plot_hist(df, 'total_bill')

# Plot horizontal counts from a value-count series.
plot_count(df['day'].value_counts())

# Plot a bar chart from an unstacked dataframe.
plot_bar(df, value='total_bill', group='day')

# Plot grouped bars after melting multiple value columns.
plot_group_bar(df, value_cols=['total_bill', 'tip'], group='day')

# Plot stacked counts for a categorical column.
plot_stacked(df, group='day', hue='sex')

# Plot violin plots with optional strip dots.
df2 = df[['time', 'total_bill']].rename(columns={'time': 'variable', 'total_bill': 'value'})
plot_violin(df2)

# Plot a box plot ordered by the group median.
plot_box(df, value='total_bill', group='day')

# Plot a pie chart from a value-count series.
plot_pie(df['day'].value_counts())

# Plot vertical counts with labels above the bars.
plot_cnt(df['day'].value_counts())

# Calculate within-bin percentages for a stacked composition chart.
df2 = sns.load_dataset('titanic').dropna(subset=['class', 'sex']).reset_index(drop=True)
calculate_pct(df2, 'class', 'sex')

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sex	female	male
class
First	43.518519	56.481481
Second	41.304348	58.695652
Third	29.327902	70.672098

# Plot stacked percentages for a bin-by-category composition.
plot_composition(df2, 'class', 'sex')

04 heatmap

from kplot.heatmap import get_similarity, plot_corr, plot_confusion_matrix

import seaborn as sns

# Set up the objects used by the examples below.
df = sns.load_dataset('titanic').dropna(subset=['age', 'fare', 'class', 'sex', 'survived']).reset_index(drop=True)
df2 = df[['age', 'fare', 'sibsp', 'parch']].head(8).copy()
df2.index = [f'row_{i}' for i in range(len(df2))]
print(df.shape)
print(df2.shape)

(714, 15)
(8, 4)

# Calculate both distance and similarity matrices for a dataframe.
get_similarity(df2)[0]

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	row_0	row_1	row_2	row_3	row_4	row_5	row_6	row_7
row_0	0.000000	66.001996	4.177993	47.657345	13.062925	54.911521	24.415786	6.714166
row_1	66.001996	0.000000	64.492435	18.429118	63.312323	25.182682	61.821302	61.188418
row_2	4.177993	64.492435	0.000000	46.073643	9.000868	52.100901	27.548548	3.910651
row_3	47.657345	18.429118	46.073643	0.000000	45.061097	19.066500	46.039121	42.780883
row_4	13.062925	63.312323	9.000868	45.061097	0.000000	47.754949	35.618122	8.803791
row_5	54.911521	25.182682	52.100901	19.066500	47.754949	0.000000	60.513388	48.906725
row_6	24.415786	61.821302	27.548548	46.039121	35.618122	60.513388	0.000000	27.089433
row_7	6.714166	61.188418	3.910651	42.780883	8.803791	48.906725	27.089433	0.000000

# Plot a square matrix with an optional triangular mask.
plot_corr(df[['age', 'fare', 'sibsp', 'parch']].corr(numeric_only=True))

# Plot a confusion matrix from target and prediction arrays.
plot_confusion_matrix(df['survived'], df['adult_male'], class_names=['False', 'True'], normalize=True)

Normalized confusion matrix

05 metrics

from kplot.metrics import plot_rank, get_AUCDF

import seaborn as sns

# Set up the objects used by the examples below.
df = sns.load_dataset('tips').dropna().sort_values('total_bill').reset_index(drop=True)
df.shape

(244, 7)

# Plot a ranked scatter and annotate the highest and lowest entries.
plot_rank(df, x='day', y='total_bill', n_hi=1, n_lo=1)

# Compute the normalized area under an empirical CDF over rank values.
get_AUCDF(df, 'total_bill', plot=False)

0.6519265042202643