Nightingale is a Python package designed for visualization and plotting. It aims to provide users with powerful tools to create insightful and informative visual representations of data.
- Easy-to-use interface for creating various types of plots.
- Customizable visualizations to suit your needs.
- Supports multiple data formats.
To install Nightingale, you can use pip:
pip install nightingaleHere's a simple example of how to use Nightingale:
The best way to use line_plot, scatter_plot, density_plot, or any other plotting function in Nightingale is to pass in a pandas DataFrame and specify the columns you want to plot.
However, you can also pass in a single pandas Series or numpy array, or a list of numbers as x and y values.
You can also pass a list or dictionary of lists of numbers or a list or dictionary of pandas Series or numpy arrays as one of x or y values.
If you pass a list or dictionary of a collection (list, Series, array) as x or y, or you pass a list of column names, the function will understand that you want to compare them, and uses colours to differentiate between them.
There are many ways to provide x and y values to the line_plot function.
import nightingale as ng
import pandas as pd
import numpy as np
# Example code to create a line plot from a pandas DataFrame
df = pd.DataFrame({
'x': [1, 2, 3, 4, 5],
'y': [1, 2, 3, 4, 5]
})
ng.line_plot(df, x='x', y='y') # returns a Figure object
ng.line_plot(df, x='x', y='y', display=True) # displays the plot and returns None
# Example code to create a line plot and using colours to differentiate between lines
df = pd.DataFrame({
'x': [1, 2, 3, 4, 5, 1, 2, 3, 4, 5],
'y': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
'group': ['a', 'a', 'a', 'a', 'a', 'b', 'b', 'b', 'b', 'b']
})
ng.line_plot(df, x='x', y='y', colour_by='group')
# Example code to create a line plot and using line types to differentiate between lines
df = pd.DataFrame({
'x': [1, 2, 3, 4, 5, 1, 2, 3, 4, 5],
'y': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
'group': ['a', 'a', 'a', 'a', 'a', 'b', 'b', 'b', 'b', 'b']
})
ng.line_plot(df, x='x', y='y', line_type_by='group')
# Example code to create a line plot and using line types and colours to differentiate between lines
df = pd.DataFrame({
'x': [1, 2, 3, 4, 5] * 4,
'y': range(20),
'group1': ['a'] * 10 + ['b'] * 10,
'group2': ['c'] * 5 + ['d'] * 5 + ['c'] * 5 + ['d'] * 5
})
ng.line_plot(df, x='x', y='y', colour_by='group1', line_type_by='group2')You can pass in a single pandas Series or numpy array or list of numbers as x and y values.
# Example code to create a line plot from a single pandas Series or numpy array or list of numbers
x_series = pd.Series([1, 2, 3, 4, 5])
y_series = pd.Series([1, 2, 3, 4, 5])
ng.line_plot(x_series, y_series)
x_array = np.array([1, 2, 3, 4, 5])
y_array = np.array([1, 2, 3, 4, 5])
ng.line_plot(x_array, y_array)
x_list = [1, 2, 3, 4, 5]
y_list = [1, 2, 3, 4, 5]
ng.line_plot(x_list, y_list)
If you pass a dataframe, you can pass in a list of column names as x and y values.
df = pd.DataFrame({
'x1': [1, 2, 3, 4, 5],
'x2': [6, 7, 8, 9, 10],
'y1': [1, 2, 3, 4, 5],
'y2': [6, 7, 8, 9, 10]
})
# This will use different colours for the two lines defined by x1 and x2
ng.line_plot(df, x=['x1', 'x2'], y='y1')
# This will use different colours for the two lines defined by y1 and y2
ng.line_plot(df, x='x1', y=['y1', 'y2'])
You can pass in a list of lists of numbers or arrays of numbers or pandas Series of numbers as x and y values.
# Example of code to create a line plot from a list of collections
x1 = [1, 2, 3, 4, 5]
x2 = [6, 7, 8, 9, 10]
y1 = [1, 2, 3, 4, 5]
y2 = [6, 7, 8, 9, 10]
# This will use different colours for the two lines defined by x1 and x2
ng.line_plot(x=[x1, x2], y=y1)
# This will use different colours for the two lines defined by y1 and y2
ng.line_plot(x=x1, y=[y1, y2])
# Example of code to create a line plot from a list of Series
x1_series = pd.Series([1, 2, 3, 4, 5])
x2_series = pd.Series([6, 7, 8, 9, 10])
y1_series = pd.Series([1, 2, 3, 4, 5])
y2_series = pd.Series([6, 7, 8, 9, 10])
# This will use different colours for the two lines defined by x1 and x2
ng.line_plot(x=[x1_series, x2_series], y=y1_series)
# This will use different colours for the two lines defined by y1 and y2
ng.line_plot(x=x1_series, y=[y1_series, y2_series])
# Example of code to create a line plot from a list of numpy arrays
x1_array = np.array([1, 2, 3, 4, 5])
x2_array = np.array([6, 7, 8, 9, 10])
y1_array = np.array([1, 2, 3, 4, 5])
y2_array = np.array([6, 7, 8, 9, 10])
# This will use different colours for the two lines defined by x1 and x2
ng.line_plot(x=[x1_array, x2_array], y=y1_array)
# This will use different colours for the two lines defined by y1 and y2
ng.line_plot(x=x1_array, y=[y1_array, y2_array])
# Example code to create a plot from a dictionary of lists
x1 = [1, 2, 3, 4, 5]
x2 = [6, 7, 8, 9, 10]
y1 = [1, 2, 3, 4, 5]
y2 = [6, 7, 8, 9, 10]
# This will use different colours for the two lines defined by x1 and x2 and uses the keys of the dictionary to label the lines in the legend
ng.line_plot(x={'group1': x1, 'group2': x2}, y=y1)
# This will use different colours for the two lines defined by y1 and y2 and uses the keys of the dictionary to label the lines in the legend
ng.line_plot(x=x1, y={'group1': y1, 'group2': y2})
# Example code to create a plot from a dictionary of Series
x1_series = pd.Series([1, 2, 3, 4, 5])
x2_series = pd.Series([6, 7, 8, 9, 10])
y1_series = pd.Series([1, 2, 3, 4, 5])
y2_series = pd.Series([6, 7, 8, 9, 10])
# This will use different colours for the two lines defined by x1 and x2 and uses the keys of the dictionary to label the lines in the legend
ng.line_plot(x={'group1': x1_series, 'group2': x2_series}, y=y1_series)
# This will use different colours for the two lines defined by y1 and y2 and uses the keys of the dictionary to label the lines in the legend
ng.line_plot(x=x1_series, y={'group1': y1_series, 'group2': y2_series})
# Example code to create a plot from a dictionary of numpy arrays
x1_array = np.array([1, 2, 3, 4, 5])
x2_array = np.array([6, 7, 8, 9, 10])
y1_array = np.array([1, 2, 3, 4, 5])
y2_array = np.array([6, 7, 8, 9, 10])
# This will use different colours for the two lines defined by x1 and x2 and uses the keys of the dictionary to label the lines in the legend
ng.line_plot(x={'group1': x1_array, 'group2': x2_array}, y=y1_array)
# This will use different colours for the two lines defined by y1 and y2 and uses the keys of the dictionary to label the lines in the legend
ng.line_plot(x=x1_array, y={'group1': y1_array, 'group2': y2_array}) The scatter_plot is almost identical to the line_plot in usage. However, it does not have line_types but has two additional arguments:
size_by: a column name or a list of numbers or a pandas Series or a numpy array or a list of pandas Series or a list of numpy arrays to use for the size of the points.size_max: the maximum size of the points.
# Example code to create a scatter plot from a pandas DataFrame
df = pd.DataFrame({
'x': [1, 2, 3, 4, 5],
'y': [1, 2, 3, 4, 5]
})
ng.scatter_plot(df, x='x', y='y')
# Example code to create a scatter plot and using size to differentiate between points
df = pd.DataFrame({
'x': [1, 2, 3, 4, 5],
'y': [1, 2, 3, 4, 5],
'z': [1, 2, 3, 4, 5]
})
ng.scatter_plot(df, x='x', y='y', size_by='z', size_max=10)
# Example code to create a scatter plot from a dictionary of collections
x1 = [1, 2, 3, 4, 5]
x2 = [6, 7, 8, 9, 10]
y1 = [1, 2, 3, 4, 5]
y2 = [6, 7, 8, 9, 10]
# This will use different colours for the two lines defined by x1 and x2 and uses the keys of the dictionary to label the lines in the legend
ng.scatter_plot(x={'group1': x1, 'group2': x2}, y=y1)
# This will use different colours for the two lines defined by y1 and y2 and uses the keys of the dictionary to label the lines in the legend
ng.scatter_plot(x=x1, y={'group1': y1, 'group2': y2})
This project is licensed under the Conditional Freedom License (CFL-1.0).
This software may NOT be used, modified, or distributed by:
- Entities opposing women's reproductive rights.
- Entities opposing LGBTQ+ rights.
- Organizations advocating for tariffs and sanctions against Canada.
- Political parties, think tanks, and advocacy groups that support authoritarianism, white supremacy, or fascism.
- Individuals and businesses affiliated with Donald Trump or his political organizations.
- Companies and organizations that deny or obstruct climate science or promote climate disinformation.
- Media outlets that propagate hate speech, conspiracy theories, or extreme political propaganda.
- Companies profiting from war, mass surveillance, and militarization.
For more details, please refer to the LICENSE file.