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# -*- coding: utf-8 -*- 

# 

# Copyright (c) 2017-2018 Spotify AB 

# 

# Licensed under the Apache License, Version 2.0 (the "License"); 

# you may not use this file except in compliance with the License. 

# You may obtain a copy of the License at 

# 

# http://www.apache.org/licenses/LICENSE-2.0 

# 

# Unless required by applicable law or agreed to in writing, software 

# distributed under the License is distributed on an "AS IS" BASIS, 

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 

# See the License for the specific language governing permissions and 

# limitations under the License. 

"""Examples""" 

 

from functools import wraps as _wraps 

from inspect import getsource as _getsource 

import numpy as np 

import pandas as pd 

 

from chartify import _core 

 

import chartify 

 

_OUTPUT_FORMAT = 'png' 

 

 

def _clean_source(source): 

source = source.split('"""')[2].replace("\t", "") 

# Replace the output variable wth its value 

source = source.replace("_OUTPUT_FORMAT", "'{}'".format(_OUTPUT_FORMAT)) 

return source 

 

 

def _print_source(f): 

"""Print code after the function docstring up until the first 

set of triple quotes""" 

 

@_wraps(f) 

def wrapper(*args, **kwargs): 

source = _getsource(f) 

print(_clean_source(source)) 

return f(*args, **kwargs) 

 

return wrapper 

 

 

@_print_source 

def plot_line(): 

""" 

Line example 

""" 

import chartify 

 

# Generate example data 

data = chartify.examples.example_data() 

 

# Sum price grouped by date 

price_by_date = ( 

data.groupby('date')['total_price'].sum() 

.reset_index() # Move 'date' from index to column 

) 

print(price_by_date.head()) 

"""Print break""" 

_line_example_1(price_by_date) 

price_by_date_and_country = _line_example_2_data(data) 

_line_example_2_chart(price_by_date_and_country) 

 

 

@_print_source 

def _line_example_1(price_by_date): 

"""# Line with datetime x axis""" 

# Plot the data 

ch = chartify.Chart(blank_labels=True, x_axis_type='datetime') 

ch.set_title("Line charts") 

ch.set_subtitle("Plot two numeric values connected by an ordered line.") 

ch.plot.line( 

# Data must be sorted by x column 

data_frame=price_by_date.sort_values('date'), 

x_column='date', 

y_column='total_price') 

ch.show(_OUTPUT_FORMAT) 

 

 

@_print_source 

def _line_example_2_data(data): 

"""# Line grouped by color""" 

price_by_date_and_country = ( 

data.groupby(['date', 'fruit'])['total_price'].sum() 

.reset_index() # Move 'date' and 'country' from index to column 

) 

print(price_by_date_and_country.head()) 

"""Print break""" 

return price_by_date_and_country 

 

 

@_print_source 

def _line_example_2_chart(price_by_date_and_country): 

"""# Line grouped by color""" 

ch = chartify.Chart(blank_labels=True, x_axis_type='datetime') 

ch.set_title("Line charts - Grouped by color") 

ch.plot.line( 

# Data must be sorted by x column 

data_frame=price_by_date_and_country.sort_values('date'), 

x_column='date', 

y_column='total_price', 

color_column='fruit') 

ch.show(_OUTPUT_FORMAT) 

 

 

plot_line.__doc__ = _core.plot.PlotNumericXY.line.__doc__ 

 

 

@_print_source 

def chart_blank(): 

""" 

""" 

import chartify 

 

# Blank charts tell you how to fill in the labels 

ch = chartify.Chart() 

ch.show(_OUTPUT_FORMAT) 

 

@_print_source 

def plot_scatter(): 

"""Scatter example 

""" 

import numpy as np 

import pandas as pd 

import chartify 

 

# Generate example data 

data = chartify.examples.example_data() 

"""Print break""" 

_scatter_example_1(data) 

_scatter_example_2(data) 

_scatter_example_3(data) 

 

 

@_print_source 

def _scatter_example_1(data): 

"""# Basic Scatter""" 

# Plot the data 

ch = chartify.Chart(blank_labels=True, x_axis_type='datetime') 

ch.plot.scatter( 

data_frame=data, 

x_column='date', 

y_column='unit_price') 

ch.set_title("Scatterplot") 

ch.set_subtitle("Plot two numeric values.") 

ch.show(_OUTPUT_FORMAT) 

 

 

@_print_source 

def _scatter_example_2(data): 

"""# Scatter with size""" 

# Plot the data 

ch = chartify.Chart(blank_labels=True, x_axis_type='datetime') 

ch.plot.scatter( 

data_frame=data, 

x_column='date', 

y_column='unit_price', 

size_column='quantity') 

ch.set_title("Scatterplot") 

ch.set_subtitle( 

"Optional 'size_column' argument for changing scatter size.") 

ch.show(_OUTPUT_FORMAT) 

 

 

@_print_source 

def _scatter_example_3(data): 

"""# Scatter with size and color""" 

# Plot the data 

ch = chartify.Chart(blank_labels=True, x_axis_type='datetime') 

ch.plot.scatter( 

data_frame=data, 

x_column='date', 

y_column='unit_price', 

size_column='quantity', 

color_column='fruit') 

ch.set_title("Scatterplot") 

ch.set_subtitle("Optional 'color_column' argument for grouping by color.") 

ch.show(_OUTPUT_FORMAT) 

 

 

plot_scatter.__doc__ = _core.plot.PlotNumericXY.scatter.__doc__ 

 

 

@_print_source 

def plot_scatter_categorical(): 

"""Scatter example 

""" 

import chartify 

 

# Generate example data 

data = chartify.examples.example_data() 

high_low = (data.groupby(['fruit'])['unit_price'] 

.agg(['max', 'min']) 

.reset_index()) 

print(high_low.head()) 

"""Print break""" 

_scatter_categorical_example(high_low) 

 

 

@_print_source 

def _scatter_categorical_example(high_low): 

"""Scatter categorical example""" 

# Plot the data 

ch = chartify.Chart(blank_labels=True, 

y_axis_type='categorical') 

ch.set_title("Scatter plot with categorical y-axis") 

ch.plot.scatter( 

data_frame=high_low, 

categorical_columns='fruit', 

numeric_column='max', 

marker='circle', 

) 

ch.plot.scatter( 

data_frame=high_low, 

categorical_columns='fruit', 

numeric_column='min', 

marker='square', 

) 

ch.show(_OUTPUT_FORMAT) 

 

plot_scatter_categorical.__doc__ = _core.plot.PlotMixedTypeXY.scatter.__doc__ 

 

 

@_print_source 

def plot_text(): 

""" 

Text example 

""" 

import chartify 

 

data = chartify.examples.example_data() 

 

# Manipulate the data 

price_and_quantity_by_country = ( 

data.groupby('country')[['total_price', 'quantity']].sum() 

.reset_index()) 

print(price_and_quantity_by_country.head()) 

"""Print break""" 

_text_example_1(price_and_quantity_by_country) 

 

 

@_print_source 

def _text_example_1(price_and_quantity_by_country): 

"""Plot text with scatter""" 

# Plot the data 

ch = chartify.Chart(blank_labels=True) 

ch.plot.scatter( 

data_frame=price_and_quantity_by_country, 

x_column='total_price', 

y_column='quantity', 

color_column='country') 

ch.style.color_palette.reset_palette_order() 

ch.plot.text( 

data_frame=price_and_quantity_by_country, 

x_column='total_price', 

y_column='quantity', 

text_column='country', 

color_column='country', 

x_offset=1, 

y_offset=-1, 

font_size='10pt') 

ch.set_title("Text") 

ch.set_subtitle("Labels for specific observations.") 

ch.show(_OUTPUT_FORMAT) 

 

 

plot_text.__doc__ = _core.plot.PlotNumericXY.text.__doc__ 

 

 

@_print_source 

def plot_area(): 

""" 

Area example 

""" 

import pandas as pd 

import chartify 

 

# Generate example data 

data = chartify.examples.example_data() 

 

total_quantity_by_month_and_fruit = (data.groupby( 

[data['date'] + pd.offsets.MonthBegin(-1), 'fruit'])['quantity'].sum() 

.reset_index().rename(columns={'date': 'month'}) 

.sort_values('month')) 

print(total_quantity_by_month_and_fruit.head()) 

"""Print break""" 

_area_example_1(total_quantity_by_month_and_fruit) 

_area_example_2(total_quantity_by_month_and_fruit) 

_plot_shaded_interval(data) 

 

 

@_print_source 

def plot_hexbin(): 

""" 

Hexbin example 

""" 

import chartify 

 

# Generate example data 

data = chartify.examples.example_data() 

 

ch = chartify.Chart(blank_labels=True, 

x_axis_type='density', 

y_axis_type='density') 

ch.set_title("Hexbin") 

ch.plot.hexbin(data_frame=data, 

x_values_column='unit_price', 

y_values_column='quantity', 

size=.2, 

orientation='pointytop') 

ch.show(_OUTPUT_FORMAT) 

 

 

@_print_source 

def _area_example_1(total_quantity_by_month_and_fruit): 

"""# Stacked area""" 

# Plot the data 

ch = chartify.Chart(blank_labels=True, x_axis_type='datetime') 

ch.set_title("Stacked area") 

ch.set_subtitle("Represent changes in distribution.") 

ch.plot.area( 

data_frame=total_quantity_by_month_and_fruit, 

x_column='month', 

y_column='quantity', 

color_column='fruit', 

stacked=True) 

ch.show(_OUTPUT_FORMAT) 

 

 

@_print_source 

def _area_example_2(total_quantity_by_month_and_fruit): 

"""# Unstacked area chart""" 

ch = chartify.Chart(blank_labels=True, x_axis_type='datetime') 

ch.set_title("Unstacked area") 

ch.set_subtitle("Show overlapping values. Automatically adjusts opacity.") 

ch.plot.area( 

data_frame=total_quantity_by_month_and_fruit, 

x_column='month', 

y_column='quantity', 

color_column='fruit', 

stacked=False) 

ch.show(_OUTPUT_FORMAT) 

 

 

@_print_source 

def _plot_shaded_interval(data): 

""" 

Shaded interval example 

""" 

 

# Sum price grouped by date 

price_by_date = (data.groupby(['date'])['total_price'].agg( 

['mean', 'std', 'count']) 

.loc['2017-12-01':].assign( 

lower_ci=lambda x: x['mean'] - 1.96 * x['std'] / x['count']**.5, 

upper_ci=lambda x: x['mean'] + 1.96 * x['std'] / x['count']**.5) 

.reset_index()) 

print(price_by_date.head()) 

"""Print break""" 

_shaded_interval_example_1(price_by_date) 

 

 

@_print_source 

def _shaded_interval_example_1(price_by_date): 

"""# Line with datetime x axis""" 

# Plot the data 

ch = chartify.Chart(blank_labels=True, x_axis_type='datetime') 

ch.set_title("Area with second_y_column") 

ch.set_subtitle( 

"Use alone or combined with line graphs to represent confidence." 

) 

ch.plot.area( 

data_frame=price_by_date, 

x_column='date', 

y_column='lower_ci', 

second_y_column='upper_ci') 

# Reset to ensure same color of line & shaded interval 

ch.style.color_palette.reset_palette_order() 

ch.plot.line( 

data_frame=price_by_date, 

x_column='date', 

y_column='mean') 

ch.show(_OUTPUT_FORMAT) 

 

 

plot_area.__doc__ = _core.plot.PlotNumericXY.area.__doc__ 

 

 

@_print_source 

def plot_bar(): 

""" 

Bar example 

""" 

import chartify 

 

# Generate example data 

data = chartify.examples.example_data() 

 

quantity_by_fruit = (data.groupby('fruit')['quantity'].sum().reset_index()) 

 

print(quantity_by_fruit.head()) 

"""Print break""" 

_bar_example_1(quantity_by_fruit) 

_bar_example_2(quantity_by_fruit) 

_bar_example_3(quantity_by_fruit) 

_bar_example_4(quantity_by_fruit) 

 

 

@_print_source 

def _bar_example_1(quantity_by_fruit): 

"""# Plot the data ordered by the numerical axis""" 

ch = chartify.Chart(blank_labels=True, x_axis_type='categorical') 

ch.set_title("Vertical bar plot") 

ch.set_subtitle("Automatically sorts by value counts.") 

ch.plot.bar( 

data_frame=quantity_by_fruit, 

categorical_columns='fruit', 

numeric_column='quantity') 

ch.show(_OUTPUT_FORMAT) 

 

 

@_print_source 

def _bar_example_2(quantity_by_fruit): 

"""# Plot the data ordered by the categorical axis labels""" 

ch = chartify.Chart(blank_labels=True, x_axis_type='categorical') 

ch.set_title("Vertical bar plot - Label sort") 

ch.set_subtitle("Set `categorical_order_by` to sort by labels") 

ch.plot.bar( 

data_frame=quantity_by_fruit, 

categorical_columns='fruit', 

numeric_column='quantity', 

categorical_order_by='labels', 

categorical_order_ascending=True) 

ch.show(_OUTPUT_FORMAT) 

 

 

@_print_source 

def _bar_example_3(quantity_by_fruit): 

"""# Plot the data with color grouping""" 

ch = chartify.Chart(blank_labels=True, y_axis_type='categorical') 

ch.set_title("Horizontal bar plot") 

ch.set_subtitle("Horizontal with color grouping") 

ch.plot.bar( 

data_frame=quantity_by_fruit, 

categorical_columns='fruit', 

numeric_column='quantity', 

color_column='fruit') 

ch.show(_OUTPUT_FORMAT) 

 

 

@_print_source 

def _bar_example_4(quantity_by_fruit): 

"""# Plot the data with labels""" 

ch = chartify.Chart(x_axis_type='categorical', blank_labels=True) 

ch.set_title("Vertical bar plot with labels") 

ch.set_subtitle("Hidden y-axis") 

ch.plot.bar( 

data_frame=quantity_by_fruit, 

categorical_columns='fruit', 

numeric_column='quantity', 

color_column='fruit') 

ch.style.color_palette.reset_palette_order() 

ch.plot.text( 

data_frame=quantity_by_fruit, 

categorical_columns='fruit', 

numeric_column='quantity', 

text_column='quantity', 

color_column='fruit') 

# Adjust the axis range to prevent clipping of the text labels. 

ch.axes.set_yaxis_range(0, 1200) 

ch.axes.hide_yaxis() 

ch.show(_OUTPUT_FORMAT) 

 

 

plot_bar.__doc__ = _core.plot.PlotMixedTypeXY.bar.__doc__ 

 

 

@_print_source 

def plot_interval(): 

""" 

Interval example 

""" 

import chartify 

 

# Generate example data 

data = chartify.examples.example_data() 

"""Print Break""" 

avg_price_with_interval = _interval_example_data(data) 

_interval_example(avg_price_with_interval) 

_interval_example2(avg_price_with_interval) 

 

 

@_print_source 

def _interval_example_data(data): 

"""Docstring""" 

avg_price_with_interval = (data.groupby('fruit')['total_price'].agg( 

['mean', 'std', 'count']) 

.assign( 

lower_ci=lambda x: x['mean'] - 1.96 * x['std'] / x['count']**.5, 

upper_ci=lambda x: x['mean'] + 1.96 * x['std'] / x['count']**.5) 

.reset_index()) 

"""Print break""" 

return avg_price_with_interval 

 

 

@_print_source 

def _interval_example(avg_price_with_interval): 

"""# Plot the data ordered by the numerical axis""" 

ch = chartify.Chart(blank_labels=True, x_axis_type='categorical') 

ch.set_title("Interval plots") 

ch.set_subtitle( 

"Represent variation. Optional `middle_column` to mark a middle point." 

) 

ch.plot.interval( 

data_frame=avg_price_with_interval, 

categorical_columns='fruit', 

lower_bound_column='lower_ci', 

upper_bound_column='upper_ci', 

middle_column='mean') 

ch.show(_OUTPUT_FORMAT) 

 

 

@_print_source 

def _interval_example2(avg_price_with_interval): 

"""# Plot the data ordered by the numerical axis""" 

ch = chartify.Chart(blank_labels=True, x_axis_type='categorical') 

ch.set_title("Combined interval plot & bar plot") 

ch.plot.bar( 

data_frame=avg_price_with_interval, 

categorical_columns='fruit', 

numeric_column='mean') 

ch.plot.interval( 

data_frame=avg_price_with_interval, 

categorical_columns='fruit', 

lower_bound_column='lower_ci', 

upper_bound_column='upper_ci') 

ch.show(_OUTPUT_FORMAT) 

 

 

plot_interval.__doc__ = _core.plot.PlotMixedTypeXY.interval.__doc__ 

 

 

@_print_source 

def plot_bar_grouped(): 

""" 

Grouped bar example. 

 

ch.plot.bar() docstring: 

""" 

import chartify 

 

# Generate example data 

data = chartify.examples.example_data() 

 

quantity_by_fruit_and_country = (data.groupby( 

['fruit', 'country'])['quantity'].sum().reset_index()) 

print(quantity_by_fruit_and_country.head()) 

"""Print break""" 

_bar_grouped_example_1(quantity_by_fruit_and_country) 

_bar_grouped_example_2(quantity_by_fruit_and_country) 

_bar_grouped_example_3(quantity_by_fruit_and_country) 

_bar_grouped_example_4(quantity_by_fruit_and_country) 

 

 

@_print_source 

def _bar_grouped_example_1(quantity_by_fruit_and_country): 

"""Docstring""" 

# Plot the data 

ch = chartify.Chart(blank_labels=True, x_axis_type='categorical') 

ch.set_title("Grouped bar chart") 

ch.set_subtitle( 

"Pass a list to group by multiple factors. Color grouped by 'fruit'") 

ch.plot.bar( 

data_frame=quantity_by_fruit_and_country, 

categorical_columns=['fruit', 'country'], 

numeric_column='quantity', 

color_column='fruit') 

ch.show(_OUTPUT_FORMAT) 

 

 

@_print_source 

def _bar_grouped_example_2(quantity_by_fruit_and_country): 

"""Docstring""" 

ch = chartify.Chart(blank_labels=True, x_axis_type='categorical') 

ch.set_title("Grouped bar chart - Color groupings") 

ch.set_subtitle( 

"Change color independent of the axis factors. Color grouped by 'country'" 

) 

ch.plot.bar( 

data_frame=quantity_by_fruit_and_country, 

categorical_columns=['fruit', 'country'], 

numeric_column='quantity', 

color_column='country') 

ch.show(_OUTPUT_FORMAT) 

 

 

@_print_source 

def _bar_grouped_example_3(quantity_by_fruit_and_country): 

"""Docstring""" 

ch = chartify.Chart(blank_labels=True, x_axis_type='categorical') 

ch.set_title("Grouped bar chart - Group hierarchy order") 

ch.set_subtitle( 

"Change chage order of 'categorical_column' list to switch grouping hierarchy." 

) 

ch.plot.bar( 

data_frame=quantity_by_fruit_and_country, 

categorical_columns=['country', 'fruit'], 

numeric_column='quantity', 

color_column='country') 

ch.axes.set_xaxis_tick_orientation('vertical') 

ch.show(_OUTPUT_FORMAT) 

 

 

@_print_source 

def _bar_grouped_example_4(quantity_by_fruit_and_country): 

"""Docstring""" 

ch = chartify.Chart(blank_labels=True, x_axis_type='categorical') 

ch.set_title("Grouped bar chart - Factor order") 

ch.set_subtitle("Change categorical order with 'categorical_order_by'.") 

ch.plot.bar( 

data_frame=quantity_by_fruit_and_country, 

categorical_columns=['country', 'fruit'], 

numeric_column='quantity', 

color_column='country', 

categorical_order_by='labels', 

categorical_order_ascending=True) 

ch.axes.set_xaxis_tick_orientation('vertical') 

ch.show(_OUTPUT_FORMAT) 

 

 

plot_bar_grouped.__doc__ += _core.plot.PlotMixedTypeXY.bar.__doc__ 

 

 

@_print_source 

def plot_bar_stacked(): 

""" 

Bar example 

""" 

import numpy as np 

import pandas as pd 

import chartify 

 

# Generate example data 

data = chartify.examples.example_data() 

 

quantity_by_fruit_and_country = (data.groupby( 

['fruit', 'country'])['quantity'].sum().reset_index()) 

print(quantity_by_fruit_and_country.head()) 

"""Print Break""" 

_bar_stacked_example_1(quantity_by_fruit_and_country) 

_bar_stacked_example_2(quantity_by_fruit_and_country) 

country_order = _bar_stacked_example_3(quantity_by_fruit_and_country) 

_bar_stacked_example_4(quantity_by_fruit_and_country, country_order) 

 

 

@_print_source 

def _bar_stacked_example_1(quantity_by_fruit_and_country): 

"""Docstring""" 

# Plot the data 

(chartify.Chart(blank_labels=True, 

x_axis_type='categorical') 

.set_title("Stacked bar chart") 

.set_subtitle("Stack columns by a categorical factor.") 

.plot.bar_stacked( 

data_frame=quantity_by_fruit_and_country, 

categorical_columns=['fruit'], 

numeric_column='quantity', 

stack_column='country', 

normalize=False) 

.show(_OUTPUT_FORMAT)) 

 

 

@_print_source 

def _bar_stacked_example_2(quantity_by_fruit_and_country): 

"""Docstring""" 

(chartify.Chart(blank_labels=True, x_axis_type='categorical') 

.set_title("Grouped bar chart - Normalized") 

.set_subtitle("Set the 'normalize' parameter for 100% bars.") 

.plot.bar_stacked( 

data_frame=quantity_by_fruit_and_country, 

categorical_columns=['fruit'], 

numeric_column='quantity', 

stack_column='country', 

normalize=True) 

.show(_OUTPUT_FORMAT)) 

 

 

@_print_source 

def _bar_stacked_example_3(quantity_by_fruit_and_country): 

"""Docstring""" 

# Get the ordered list of quanity by country to order the stacks. 

country_order = ( 

quantity_by_fruit_and_country.groupby('country')['quantity'].sum() 

.sort_values(ascending=False).index) 

(chartify.Chart(blank_labels=True, x_axis_type='categorical') 

.set_title("Grouped bar chart - Ordered stack") 

.set_subtitle("Change the order of the stack with `stack_order`.") 

.plot.bar_stacked( 

data_frame=quantity_by_fruit_and_country, 

categorical_columns=['fruit'], 

numeric_column='quantity', 

stack_column='country', 

normalize=True, 

stack_order=country_order) 

.show(_OUTPUT_FORMAT)) 

"""Print break""" 

return country_order 

 

 

@_print_source 

def _bar_stacked_example_4(quantity_by_fruit_and_country, country_order): 

"""Docstring""" 

# Add a column for labels. 

# Note: Null labels will not be added to the chart. 

quantity_by_fruit_and_country['label'] = np.where( 

quantity_by_fruit_and_country['country'].isin(['US', 'CA']), 

quantity_by_fruit_and_country['quantity'], 

None) 

 

(chartify.Chart(blank_labels=True, x_axis_type='categorical') 

.set_title("Stacked bar with labels") 

.set_subtitle("") 

.plot.bar_stacked( 

data_frame=quantity_by_fruit_and_country, 

categorical_columns=['fruit'], 

numeric_column='quantity', 

stack_column='country', 

normalize=True, 

stack_order=country_order) 

.plot.text_stacked( 

data_frame=quantity_by_fruit_and_country, 

categorical_columns=['fruit'], 

numeric_column='quantity', 

stack_column='country', 

text_column='label', 

normalize=True, 

stack_order=country_order, 

# Set the text color otherwise it will take 

# The next color in the color palette. 

text_color='white' 

) 

.show(_OUTPUT_FORMAT)) 

 

 

plot_bar_stacked.__doc__ = _core.plot.PlotMixedTypeXY.bar_stacked.__doc__ 

 

 

@_print_source 

def plot_lollipop(): 

""" 

Lollipop example 

""" 

import chartify 

 

# Generate example data 

data = chartify.examples.example_data() 

 

quantity_by_fruit_and_country = (data.groupby( 

['fruit', 'country'])['quantity'].sum().reset_index()) 

print(quantity_by_fruit_and_country.head()) 

"""Print break""" 

_lollipop_example_1(quantity_by_fruit_and_country) 

 

 

@_print_source 

def _lollipop_example_1(quantity_by_fruit_and_country): 

"""Docstring""" 

# Plot the data 

ch = chartify.Chart(blank_labels=True, y_axis_type='categorical') 

ch.set_title("Lollipop chart") 

ch.set_subtitle("Same options as bar plot") 

ch.plot.lollipop( 

data_frame=quantity_by_fruit_and_country, 

categorical_columns=['country', 'fruit'], 

numeric_column='quantity', 

color_column='country') 

ch.show(_OUTPUT_FORMAT) 

 

 

plot_lollipop.__doc__ = _core.plot.PlotMixedTypeXY.lollipop.__doc__ 

 

 

@_print_source 

def plot_parallel(): 

""" 

Parallel coordinate plot example 

""" 

import chartify 

 

# Generate example data 

data = chartify.examples.example_data() 

 

total_quantity_by_fruit_and_country = (data.groupby( 

['fruit', 'country'])['quantity'].sum().reset_index()) 

print(total_quantity_by_fruit_and_country.head()) 

"""Print break""" 

_parallel_example_1(total_quantity_by_fruit_and_country) 

 

 

@_print_source 

def _parallel_example_1(total_quantity_by_fruit_and_country): 

"""# Parallel with datetime x axis""" 

# Plot the data 

ch = chartify.Chart(blank_labels=True, x_axis_type='categorical') 

ch.set_title("Parallel coordinate charts") 

ch.set_subtitle("") 

ch.plot.parallel( 

data_frame=total_quantity_by_fruit_and_country, 

categorical_columns='fruit', 

numeric_column='quantity', 

color_column='country') 

ch.show(_OUTPUT_FORMAT) 

 

 

plot_parallel.__doc__ = _core.plot.PlotMixedTypeXY.parallel.__doc__ 

 

 

@_print_source 

def plot_histogram(): 

""" 

Histogram example 

""" 

import chartify 

 

# Generate example data 

data = chartify.examples.example_data() 

 

print(data.head()) 

"""Print break""" 

_histogram_example(data) 

_histogram_example2(data) 

 

 

@_print_source 

def _histogram_example(data): 

"""# Histogram""" 

# Plot the data 

ch = chartify.Chart(blank_labels=True, y_axis_type='density') 

ch.set_title("Histogram") 

ch.set_subtitle("") 

ch.plot.histogram( 

data_frame=data, 

values_column='unit_price', 

bins=50) 

ch.show(_OUTPUT_FORMAT) 

 

 

@_print_source 

def _histogram_example2(data): 

"""# Horizontal histogram""" 

ch = chartify.Chart(blank_labels=True, x_axis_type='density') 

ch.set_title("Horizontal histogram with grouping") 

ch.set_subtitle("") 

ch.plot.histogram( 

data_frame=data, 

values_column='unit_price', 

color_column='fruit') 

ch.show(_OUTPUT_FORMAT) 

 

 

plot_histogram.__doc__ = _core.plot.PlotNumericDensityXY.histogram.__doc__ 

 

 

@_print_source 

def plot_kde(): 

""" 

KDE example 

""" 

import chartify 

 

# Generate example data 

data = chartify.examples.example_data() 

 

print(data.head()) 

"""Print break""" 

_kde_example(data) 

_kde_example2(data) 

 

 

@_print_source 

def _kde_example(data): 

"""# Parallel with datetime x axis""" 

# Plot the data 

ch = chartify.Chart(blank_labels=True, y_axis_type='density') 

ch.set_title("KDE plot") 

ch.plot.kde( 

data_frame=data, 

values_column='unit_price', 

color_column='fruit') 

ch.show(_OUTPUT_FORMAT) 

 

 

@_print_source 

def _kde_example2(data): 

"""# Parallel with datetime x axis""" 

# Plot the data 

ch = chartify.Chart(blank_labels=True, y_axis_type='density') 

ch.set_title("KDE plot + Histogram") 

ch.plot.kde( 

data_frame=data, 

values_column='unit_price', 

color_column='fruit') 

ch.style.color_palette.reset_palette_order() 

ch.plot.histogram( 

data_frame=data, 

values_column='unit_price', 

color_column='fruit', 

method='density') 

ch.show(_OUTPUT_FORMAT) 

 

 

plot_histogram.__doc__ = _core.plot.PlotNumericDensityXY.kde.__doc__ 

 

 

@_print_source 

def plot_heatmap(): 

""" 

Bar example 

""" 

import chartify 

 

# Generate example data 

data = chartify.examples.example_data() 

 

average_price_by_fruit_and_country = (data.groupby( 

['fruit', 'country'])['total_price'].mean().reset_index()) 

"""Print break""" 

_heatmap_example_1(average_price_by_fruit_and_country) 

 

 

@_print_source 

def _heatmap_example_1(average_price_by_fruit_and_country): 

"""Docstring""" 

# Plot the data 

(chartify.Chart( 

blank_labels=True, 

x_axis_type='categorical', 

y_axis_type='categorical') 

.plot.heatmap( 

data_frame=average_price_by_fruit_and_country, 

x_column='fruit', 

y_column='country', 

color_column='total_price', 

text_column='total_price', 

text_color='white') 

.axes.set_xaxis_label('Fruit') 

.axes.set_yaxis_label('Country') 

.set_title('Heatmap') 

.set_subtitle("Plot numeric value grouped by two categorical values") 

.show(_OUTPUT_FORMAT)) 

 

 

plot_heatmap.__doc__ = _core.plot.PlotCategoricalXY.heatmap.__doc__ 

 

 

@_print_source 

def plot_radar_area(): 

"""Radar area example 

""" 

import chartify 

 

# Generate example data 

data = chartify.examples.example_data() 

total_by_fruit_and_country = data.groupby(['fruit', 'country'])['quantity'].sum().reset_index() 

print(total_by_fruit_and_country.head()) 

"""Print break""" 

_radar_area_example_1(total_by_fruit_and_country) 

 

 

@_print_source 

def _radar_area_example_1(total_by_fruit_and_country): 

"""# Basic Scatter""" 

ch = chartify.RadarChart(True, layout='slide_50%') 

ch.set_title('Radar Area Chart') 

ch.set_subtitle("Each vertex plotted counterclockwise starting from top") 

ch.plot.text(total_by_fruit_and_country.groupby('country')['quantity'].max().reset_index(), 

'quantity', 

text_column='country', 

text_align='center') 

ch.plot.area(total_by_fruit_and_country, 'quantity', color_column='fruit') 

ch.axes.hide_yaxis() 

ch.axes.hide_xaxis() 

ch.set_legend_location('outside_bottom') 

ch.show(_OUTPUT_FORMAT) 

 

 

@_print_source 

def plot_radar_radius(): 

"""Radar area example 

""" 

import chartify 

from itertools import product 

# Generate example data 

data = chartify.examples.example_data() 

 

total_by_country = data.groupby(['country'])['quantity'].sum().reset_index() 

countries = total_by_country.country.unique() 

grid_values = product(range(0, 1200, 200), countries) 

grid_df = pd.DataFrame.from_records(grid_values, columns=['quantity', 'country']) 

quantity_label = grid_df.groupby('quantity')[['quantity']].min().reset_index(drop=True) 

 

ch = chartify.RadarChart(True, layout='slide_50%') 

ch.set_title('Radar Radius Chart') 

 

ch.style.set_color_palette('categorical', ['grey']) 

# Plot quantity labels 

ch.plot.text(quantity_label, 

'quantity', 

text_column='quantity', 

color_column='quantity', 

font_size='8pt') 

# Plot perimeter grid 

ch.plot.perimeter(grid_df, 

radius_column='quantity', 

color_column='quantity', 

line_width=1) 

ch.style.set_color_palette('categorical') 

 

# Plot text labels 

ch.plot.text(total_by_country, 

'quantity', 

text_column='country', 

text_align='center') 

ch.style.color_palette.reset_palette_order() 

# Plot the radius 

ch.plot.radius(total_by_country, 'quantity') 

ch.axes.hide_yaxis() 

ch.axes.hide_xaxis() 

ch.set_legend_location(None) 

ch.show(_OUTPUT_FORMAT) 

 

 

@_print_source 

def chart_second_axis(): 

""" 

Docstring 

""" 

import chartify 

 

# Generate example data 

data = chartify.examples.example_data() 

 

# Sum price grouped by date 

price_by_date = ( 

data.groupby('date')['total_price'].sum() 

.reset_index() # Move 'date' from index to column 

) 

price_by_date['triple_total_price'] = price_by_date['total_price'] * 3 

price_by_date = price_by_date.sort_values('date') 

print(price_by_date.head()) 

"""Print break""" 

_second_axis_example(price_by_date) 

 

 

@_print_source 

def _second_axis_example(price_by_date): 

"""Docstring""" 

# Initialize chart with second_y_axis=True 

ch = chartify.Chart(blank_labels=True, 

x_axis_type='datetime', 

y_axis_type='linear', 

second_y_axis=True) 

ch.set_title("Second Y axis") 

# Plot the first axis 

ch.plot.line( 

data_frame=price_by_date, 

x_column='date', 

y_column='total_price') 

ch.axes.set_yaxis_range(0, 50) 

ch.axes.set_yaxis_label('First axis label') 

ch.axes.set_yaxis_tick_format('0a') 

 

# Plot the second axis 

ch.second_axis.axes.set_yaxis_range(0, 80) 

ch.second_axis.axes.set_yaxis_label('Second axis label') 

ch.second_axis.plot.line( 

# Data must be sorted by x column 

data_frame=price_by_date, 

x_column='date', 

y_column='triple_total_price') 

ch.second_axis.axes.set_yaxis_tick_format('0.0') 

ch.show(_OUTPUT_FORMAT) 

 

 

@_print_source 

def style_color_palette_accent(): 

""" 

Color palette 

""" 

import numpy as np 

import pandas as pd 

import chartify 

 

# Generate example data 

data = pd.DataFrame({'x': list(range(100))}) 

data['y'] = data['x'] * np.random.normal(size=100) 

data['z'] = np.random.choice([2, 4, 5], size=100) 

data['country'] = np.random.choice( 

['US', 'GB', 'CA', 'JP', 'BR'], size=100) 

 

# Plot the data 

ch = chartify.Chart(blank_labels=True) 

ch.style.set_color_palette('accent', accent_values=['US', 'CA']) 

ch.plot.scatter( 

data_frame=data, 

x_column='x', 

y_column='y', 

size_column='z', 

color_column='country') 

ch.set_title("Accent color palette") 

ch.set_subtitle( 

"Highlight specific color values or assign specific colors to values.") 

ch.show(_OUTPUT_FORMAT) 

 

 

@_print_source 

def style_color_palette_custom(): 

""" 

Custom color palette 

""" 

import chartify 

 

# Generate example data 

data = chartify.examples.example_data() 

 

# Create a new custom palette 

chartify.color_palettes.create_palette(colors=['#ff0000', 'yellow', 

'purple', 'orange'], 

palette_type='categorical', 

name='custom palette') 

 

ch = chartify.Chart(blank_labels=True) 

# Apply the custom palette 

ch.style.set_color_palette('categorical', 'custom palette') 

ch.plot.scatter( 

data_frame=data, 

x_column='unit_price', 

y_column='quantity', 

color_column='fruit') 

ch.set_title("Custom color palette") 

ch.show(_OUTPUT_FORMAT) 

 

def example_data(): 

"""Data set used in Chartify examples. 

""" 

import numpy as np 

import pandas as pd 

 

np.random.seed(1) 

N_SAMPLES = 1000 

 

example_data = pd.DataFrame() 

date_range = pd.date_range('2017-01-01', '2017-12-31') 

 

COUNTRIES, COUNTRY_P = ['US', 'GB', 'CA', 'JP', 

'BR'], [.35, .17, .23, .15, .1] 

 

FRUIT = ['Orange', 'Apple', 'Banana', 'Grape'] 

PRICE = [.5, 1., .25, 2.] 

fruit_price_map = dict(list(zip(FRUIT, PRICE))) 

day_probabilities = np.random.dirichlet(list(range(1, 366))) 

example_data['date'] = np.random.choice( 

date_range, p=day_probabilities, size=N_SAMPLES) 

 

COUNTRY_FRUIT_P = { 

c: np.random.dirichlet([len(FRUIT)] * len(FRUIT)) 

for c in COUNTRIES 

} 

example_data['country'] = np.random.choice( 

COUNTRIES, p=COUNTRY_P, size=N_SAMPLES) 

 

example_data['fruit'] = example_data['country'].apply( 

lambda x: np.random.choice(FRUIT, p=COUNTRY_FRUIT_P[x])) 

 

example_data['unit_price'] = example_data['fruit'].map(fruit_price_map) * ( 

1.0 + np.random.normal(0, .1, size=N_SAMPLES)) 

example_data['quantity'] = example_data['unit_price'].apply(lambda x: max(0, np.random.poisson(max(3. - x*1.25, 0)) + 1)) 

example_data['total_price'] = (example_data['unit_price'] 

* example_data['quantity']) 

return example_data 

 

 

@_print_source 

def style_color_palette_categorical(): 

""" 

Color palette 

""" 

import numpy as np 

import pandas as pd 

import chartify 

 

# Generate example data 

data = pd.DataFrame({'x': list(range(100))}) 

data['y'] = data['x'] * np.random.normal(size=100) 

data['z'] = np.random.choice([2, 4, 5], size=100) 

data['country'] = np.random.choice( 

['US', 'GB', 'CA', 'JP', 'BR'], size=100) 

 

# Plot the data 

ch = chartify.Chart(blank_labels=True) 

ch.style.set_color_palette(palette_type='categorical') 

ch.plot.scatter( 

data_frame=data, 

x_column='x', 

y_column='y', 

color_column='country') 

ch.set_title("Categorical color palette type") 

ch.set_subtitle( 

"Default palette type. Use to differentiate categorical series.") 

ch.show(_OUTPUT_FORMAT) 

"""Line break""" 

_categorical_example_2(data) 

 

 

@_print_source 

def _categorical_example_2(data): 

"""Docstring""" 

# Plot the data 

ch = chartify.Chart(blank_labels=True) 

ch.style.set_color_palette( 

palette_type='categorical',) 

ch.plot.scatter( 

data_frame=data, 

x_column='x', 

y_column='y', 

color_column='country') 

ch.set_title( 

"Pass 'palette' parameter to .set_color_palette() to change palette colors." 

) 

ch.set_subtitle("") 

ch.show(_OUTPUT_FORMAT) 

 

 

@_print_source 

def style_color_palette_sequential(): 

""" 

Color palette sequential 

""" 

import numpy as np 

import pandas as pd 

import chartify 

 

data = pd.DataFrame({'time': pd.date_range('2015-01-01', '2018-01-01')}) 

n_days = len(data) 

data['1st'] = np.array(list(range(n_days))) + np.random.normal( 

0, 10, size=n_days) 

data['2nd'] = np.array(list(range(n_days))) + np.random.normal( 

0, 10, size=n_days) + 200 

data['3rd'] = np.array(list(range(n_days))) + np.random.normal( 

0, 10, size=n_days) + 500 

data['4th'] = np.array(list(range(n_days))) + np.random.normal( 

0, 10, size=n_days) + 700 

data['5th'] = np.array(list(range(n_days))) + np.random.normal( 

0, 10, size=n_days) + 800 

data['6th'] = np.array(list(range(n_days))) + np.random.normal( 

0, 10, size=n_days) + 1000 

data = pd.melt( 

data, 

id_vars=['time'], 

value_vars=data.columns[1:], 

value_name='y', 

var_name=['grouping']) 

 

# Plot the data 

ch = chartify.Chart(blank_labels=True, x_axis_type='datetime') 

ch.style.set_color_palette(palette_type='sequential') 

ch.plot.line( 

data_frame=data.sort_values('time'), 

x_column='time', 

y_column='y', 

color_column='grouping') 

ch.set_title("Sequential color palette type") 

ch.set_subtitle("Palette type for sequential ordered dimensions") 

ch.show(_OUTPUT_FORMAT) 

 

 

@_print_source 

def style_color_palette_diverging(): 

""" 

Color palette sequential 

""" 

import numpy as np 

import pandas as pd 

import chartify 

 

data = pd.DataFrame({'time': pd.date_range('2015-01-01', '2018-01-01')}) 

n_days = len(data) 

data['Very Unlikely'] = np.array(list(range(n_days))) + np.random.normal( 

0, 10, size=n_days) 

data['Unlikely'] = np.array(list(range(n_days))) + np.random.normal( 

0, 10, size=n_days) + 200 

data['Neutral'] = np.array(list(range(n_days))) + np.random.normal( 

0, 10, size=n_days) + 500 

data['Likely'] = np.array(list(range(n_days))) + np.random.normal( 

0, 10, size=n_days) + 700 

data['Very Likely'] = np.array(list(range(n_days))) + np.random.normal( 

0, 10, size=n_days) + 800 

data = pd.melt( 

data, 

id_vars=['time'], 

value_vars=data.columns[1:], 

value_name='y', 

var_name=['grouping']) 

 

# Plot the data 

 

ch = chartify.Chart(blank_labels=True, x_axis_type='datetime') 

ch.style.set_color_palette(palette_type='diverging') 

color_order = [ 

'Very Unlikely', 'Unlikely', 'Neutral', 'Likely', 'Very Likely' 

] 

ch.plot.line( 

data_frame=data.sort_values('time'), 

x_column='time', 

y_column='y', 

color_column='grouping', 

color_order=color_order) # Your data must be sorted 

ch.set_title("Diverging color palette type") 

ch.set_subtitle("Palette type for diverging ordered dimensions") 

ch.show(_OUTPUT_FORMAT) 

 

 

@_print_source 

def callout_line(): 

""" 

Line example 

""" 

import chartify 

 

# Generate example data 

data = chartify.examples.example_data() 

 

# Plot the data 

(chartify.Chart(blank_labels=True) 

.plot.scatter( 

data_frame=data, 

x_column='unit_price', 

y_column='total_price') 

.callout.line(2) # Callout horizontal line 

.callout.line(1, 'height') # Callout vertical line 

.set_title('Line callout') 

.set_subtitle("Callout lines on either axis") 

.show(_OUTPUT_FORMAT)) 

 

 

@_print_source 

def callout_text(): 

""" 

Line segment 

""" 

import chartify 

 

# Generate example data 

data = chartify.examples.example_data() 

 

# Plot the data 

ch = chartify.Chart(blank_labels=True) 

ch.plot.scatter( 

data_frame=data, 

x_column='unit_price', 

y_column='total_price') 

ch.callout.text("Description of what is\ngoing on in this chart!", 0, 6) 

ch.set_title("Text callout") 

ch.set_subtitle("Add narrative to your chart.") 

ch.show(_OUTPUT_FORMAT) 

 

 

callout_text.__doc__ = _core.callout.Callout.text.__doc__ 

 

 

@_print_source 

def callout_box(): 

""" 

Box example 

""" 

import chartify 

 

# Generate example data 

data = chartify.examples.example_data() 

 

# Plot the data 

(chartify.Chart(blank_labels=True) 

.plot.scatter( 

data_frame=data, 

x_column='total_price', 

y_column='unit_price') 

.callout.box(top=1, bottom=-1, color='red') 

.callout.box(top=2, left=4, color='blue') 

.callout.box(bottom=2, right=3, color='green') 

.set_title("Shaded area callout") 

.set_subtitle("Highlight notable areas of your chart") 

.show(_OUTPUT_FORMAT)) 

 

 

callout_box.__doc__ = _core.callout.Callout.box.__doc__ 

 

 

@_print_source 

def axes_axis_type(): 

""" 

Axis type examples 

""" 

import numpy as np 

import pandas as pd 

import chartify 

 

# Generate example data 

data = chartify.examples.example_data() 

 

# Plot the data 

(chartify.Chart(blank_labels=True, x_axis_type='log') 

.plot.scatter( 

data_frame=data, 

x_column='total_price', 

y_column='quantity') 

.set_subtitle( 

"Set axis type for auto handling of categorical, datetime, linear, or log values." 

) 

.set_title("Axis Type") 

.show(_OUTPUT_FORMAT)) 

 

 

# axis_type.__doc__ = _core.axes.Axes.set_xaxis_type.__doc__ 

 

 

@_print_source 

def axes_axis_tick_format(): 

""" 

Axis scale examples 

""" 

import numpy as np 

import pandas as pd 

import chartify 

 

# Generate example data 

data = chartify.examples.example_data() 

data['%_sales'] = data['quantity'] / data['quantity'].sum() 

 

# Plot the data 

(chartify.Chart(blank_labels=True) 

.plot.scatter( 

data_frame=data, 

x_column='%_sales', 

y_column='unit_price') 

.axes.set_yaxis_tick_format("$0.00") 

.axes.set_xaxis_tick_format("0.00%") 

.set_subtitle("Format ticks on either axis to set units or precision") 

.set_title("Axis tick format").show(_OUTPUT_FORMAT)) 

 

 

# tick_format.__doc__ = _core.axes.Axes.format_xaxis_tick_labels.__doc__ 

 

 

@_print_source 

def axes_axis_tick_values(): 

""" 

Axis scale examples 

""" 

import pandas as pd 

import chartify 

 

# Generate example data 

data = chartify.examples.example_data() 

 

# Plot the data 

ch = chartify.Chart(blank_labels=True, x_axis_type='datetime') 

ch.plot.scatter(data, 'date', 'quantity') 

ch.set_title("Axis tick values") 

ch.set_subtitle( 

"Pass a list of values or use pd.date_range for datetime axes") 

# Use pd.date_range to generate a range of dates 

# at the start of each month 

ch.axes.set_xaxis_tick_values( 

pd.date_range('2017-01-01', '2018-01-01', freq='MS')) 

ch.axes.set_yaxis_tick_values(list(range(0, 8, 2))) 

ch.show(_OUTPUT_FORMAT) 

 

 

@_print_source 

def chart_labels(): 

""" 

Chart label examples 

""" 

import numpy as np 

import pandas as pd 

import chartify 

 

# Generate example data 

data = chartify.examples.example_data() 

 

apple_prices = (data[data['fruit'] == 'Apple'] 

.groupby(['quantity'])['unit_price'].mean().reset_index()) 

# Plot the data with method chaining 

(chartify.Chart(blank_labels=True) 

.plot.scatter(apple_prices, 'quantity', 'unit_price') 

.set_title( 

"Quantity decreases as price increases. <-- Use title for takeaway.") 

.set_subtitle( 

"Quantity vs. Price. <-- Use subtitle for data description.") 

.axes.set_xaxis_label("Quantity per sale (Apples)") 

.axes.set_yaxis_label("Price ($)") 

.axes.set_yaxis_tick_format("$0.00") 

.show(_OUTPUT_FORMAT)) 

 

 

@_print_source 

def chart_layouts(): 

""" 

Layout examples 

""" 

import numpy as np 

import pandas as pd 

import chartify 

 

# Generate example data 

data = pd.DataFrame({'Price': list(range(100))}) 

data['Demand'] = 100 + -.5 * data['Price'] + np.random.normal(size=100) 

 

layouts = ['slide_100%', 'slide_75%', 'slide_50%', 'slide_25%'] 

 

def display_layout(layout): 

(chartify.Chart( 

layout=layout) # Assign the layout when instantiating the chart. 

.plot.scatter( 

data_frame=data, 

x_column='Price', 

y_column='Demand') 

.set_title("Slide layout: '{}'".format(layout)) 

.set_subtitle("Demand vs. Price.") 

.set_source_label("") 

.axes.set_xaxis_label("Demand (# Users)") 

.axes.set_yaxis_label("Price ($)") 

.show(_OUTPUT_FORMAT)) 

 

[display_layout(layout) for layout in layouts] 

 

 

@_print_source 

def chart_show(): 

""" 

Docstring 

""" 

import numpy as np 

import pandas as pd 

import chartify 

 

# Generate example data 

data = pd.DataFrame({'x': list(range(100))}) 

data['y'] = data['x'] * np.random.normal(size=100) 

data['z'] = np.random.choice([2, 4, 5], size=100) 

data['country'] = np.random.choice( 

['US', 'GB', 'CA', 'JP', 'BR'], size=100) 

 

# Plot the data 

ch = chartify.Chart(blank_labels=True) 

ch.plot.scatter( 

data_frame=data, 

x_column='x', 

y_column='y', 

size_column='z', 

color_column='country') 

ch.set_title( 

'ch.show(): Faster rendering with HTML. Recommended while drafting.') 

ch.set_subtitle('No watermark. Does not display on github.') 

ch.show('html') # Show with HTML 

 

ch.set_title( 

'ch.show("png"): Return a png file for easy copying into slides') 

ch.set_subtitle('Will display on github.') 

ch.show('png') # Show with PNG 

 

chart_show.__doc__ = _core.chart.Chart.show.__doc__ 

 

 

@_print_source 

def chart_save(): 

""" 

Docstring 

""" 

import chartify 

 

# Plot the data 

ch = chartify.Chart(blank_labels=True) 

ch.set_title( 

'ch.show(): Save chart to html, png, or svg.') 

ch.save('saved_chart_example.html', format='html') # Save to html 

 

chart_save.__doc__ = _core.chart.Chart.save.__doc__