Barplots

Python package to easily make barplots from multi-indexed dataframes.

How do I install this package?

As usual, just download it using pip:

pip install barplots

Documentation

Most methods, in particular those exposed to user usage, are provided with docstrings. Consider reading these docstrings to learn about the most recent updates to the library.

Examples of the DataFrame structure

The dataframe to be provided to the barplots library may look like the following:

| miss_rate | fall_out | mcc | evaluation_type | unbalance | graph_name | normalization_name | |------------|----------|-----------|-----------------|-----------|----------------------------|--------------------| | 0.0332031 | 0.705078 | 0.353357 | train | 10 | AlligatorSinensis | Traditional | | 0.240234 | 0.478516 | 0.289591 | train | 1 | CanisLupus | Right Laplacian | | 0.0253906 | 0.931641 | 0.101643 | train | 100 | AlligatorSinensis | Right Laplacian | | 0.121094 | 0.699219 | 0.220219 | train | 10 | HomoSapiens | Traditional | | 0.0136719 | 0.292969 | 0.722095 | test | 1 | CanisLupus | Right Laplacian | | 0.0605469 | 0.90625 | 0.0622185 | test | 10 | AmanitaMuscariaKoideBx008 | Traditional | | 0.0078125 | 0.4375 | 0.614287 | train | 100 | AmanitaMuscariaKoideBx008 | Traditional | | 0.171875 | 0.869141 | -0.0572194| train | 100 | AlligatorSinensis | Traditional | | 0.0859375 | 0.810547 | 0.150206 | train | 10 | MusMusculus | Right Laplacian | | 0.0273438 | 0.646484 | 0.415357 | test | 10 | MusMusculus | Right Laplacian |

Specifically, in this example, we may create bar plots for the features Miss rate, fallout, and Matthew Correlation Coefficient by grouping on the evaluation_type, unbalance, graph_name, and normalization_name columns.

An example CSV file can be seen here.

Usage examples

Here follows a set of examples of common usages. Basically, every graph shows either the same data or a mean based on the provided group by indices. Choose whatever representation is best for visualizing your data, as one is not necessarily better than another for every dataset.

Note: The data used in the following examples are randomly generated for testing purposes. DO NOT consider these values as valid results for experiments using the same labels (cell lines, etc.), which are only used to show possible usages.

For every example, the considered dataframe df is loaded as follows:

import pandas as pd

df = pd.read_csv("tests/test_case.csv")

Also, for every example, the custom_defaults used to sanitize the labels specific to the dataset is:

custom_defaults = {
    "P": "promoters",
    "E": "enhancers",
    "A": "active ",
    "I": "inactive ",
    "+": " and ",
    "": "anything",
    "Validation": "val"
}

Horizontal Example A

In the following example, we will plot the bars horizontally, rotating the group labels by 90 degrees, and displaying the bar labels as a shared legend.

from barplots import barplots
import pandas as pd

df = pd.read_csv("tests/test_case.csv")
custom_defaults = {
    "P": "promoters",
    "E": "enhancers",
    "A": "active ",
    "I": "inactive ",
    "+": " and ",
    "": "anything",
    "Validation": "val"
}

barplots(
    df,
    groupby=["task", "model"],
    orientation="horizontal",
    show_legend=True,
    minor_rotation=90,
    custom_defaults=custom_defaults
)

Result can be seen here.

Horizontal Example B

In this example, we will plot the top index as multiple subplots with horizontal bars, rotating the group labels by 90 degrees, and displaying the bar labels as a shared legend.

from barplots import barplots
import pandas as pd

df = pd.read_csv("tests/test_case.csv")
custom_defaults = {
    "P": "promoters",
    "E": "enhancers",
    "A": "active ",
    "I": "inactive ",
    "+": " and ",
    "": "anything",
    "Validation": "val"
}

barplots(
    df,
    groupby=["cell_line", "task", "model"],
    orientation="horizontal",
    show_legend=True,
    subplots=True,
    minor_rotation=90,
    custom_defaults=custom_defaults
)

Horizontal Example C

In this example, we will plot horizontal bars, rotating the top group labels by 90 degrees, and displaying the bar labels as minor ticks.

from barplots import barplots
import pandas as pd

df = pd.read_csv("tests/test_case.csv")
custom_defaults = {
    "P": "promoters",
    "E": "enhancers",
    "A": "active ",
    "I": "inactive ",
    "+": " and ",
    "": "anything",
    "Validation": "val"
}

barplots(
    df,
    groupby=["task", "model"],
    orientation="horizontal",
    show_legend=False,
    major_rotation=90,
    custom_defaults=custom_defaults
)

Result can be seen here.

Horizontal Example D

In this example, we will plot the top index as multiple subplots with horizontal bars, rotating the group labels by 90 degrees, and displaying the bar labels as minor ticks.

from barplots import barplots
import pandas as pd

df = pd.read_csv("tests/test_case.csv")
custom_defaults = {
    "P": "promoters",
    "E": "enhancers",
    "A": "active ",
    "I": "inactive ",
    "+": " and ",
    "": "anything",
    "Validation": "val"
}

barplots(
    df,
    groupby=["cell_line", "task", "model"],
    orientation="horizontal",
    show_legend=False,
    major_rotation=90,
    subplots=True,
    custom_defaults=custom_defaults
)

Vertical Example A

In this example, we will plot the bars vertically and display the bar labels as a shared legend.

from barplots import barplots
import pandas as pd

df = pd.read_csv("tests/test_case.csv")
custom_defaults = {
    "P": "promoters",
    "E": "enhancers",
    "A": "active ",
    "I": "inactive ",
    "+": " and ",
    "": "anything",
    "Validation": "val"
}

barplots(
    df,
    groupby=["task", "model"],
    orientation="vertical",
    show_legend=True,
    custom_defaults=custom_defaults
)

Result can be seen here.

Vertical Example B

In this example, we will plot the top index as multiple subplots with vertical bars, displaying the bar labels as a shared legend.

from barplots import barplots
import pandas as pd

df = pd.read_csv("tests/test_case.csv")
custom_defaults = {
    "P": "promoters",
    "E": "enhancers",
    "A": "active ",
    "I": "inactive ",
    "+": " and ",
    "": "anything",
    "Validation": "val"
}

barplots(
    df,
    groupby=["cell_line", "task", "model"],
    orientation="vertical",
    show_legend=True,
    subplots=True,
    custom_defaults=custom_defaults
)

Vertical Example C

In this example, we will plot vertical bars, rotating the minor group labels by 90 degrees, and displaying the bar labels as minor ticks.

from barplots import barplots
import pandas as pd

df = pd.read_csv("tests/test_case.csv")
custom_defaults = {
    "P": "promoters",
    "E": "enhancers",
    "A": "active ",
    "I": "inactive ",
    "+": " and ",
    "": "anything",
    "Validation": "val"
}

barplots(
    df,
    groupby=["task", "model"],
    orientation="vertical",
    show_legend=False,
    minor_rotation=90,
    custom_defaults=custom_defaults
)

Result can be seen here.

Vertical Example D

In this example, we will plot the top index as multiple subplots with vertical bars, rotating the minor group labels by 90 degrees, and displaying the bar labels as minor ticks.

from barplots import barplots
import pandas as pd

df = pd.read_csv("tests/test_case.csv")
custom_defaults = {
    "P": "promoters",
    "E": "enhancers",
    "A": "active ",
    "I": "inactive ",
    "+": " and ",
    "": "anything",
    "Validation": "val"
}

barplots(
    df,
    groupby=["cell_line", "task", "model"],
    orientation="vertical",
    show_legend=False,
    minor_rotation=90,
    subplots=True,
    custom_defaults=custom_defaults
)