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))
def test_multi_series_zero_baseline_unstacked_missing(self):
"""Area plot tests"""
test_data = self.data_missing
ch = chartify.Chart()
ch.plot.area(test_data, 'x', 'upper', color_column='category')
assert (np.array_equal(chart_data(ch, 'a')['x'], [1, 2, 3, 3, 2, 1]))
assert (np.array_equal(
chart_data(ch, 'a')['upper'], [20., 30., 40., 0., 0., 0.]))
assert (np.array_equal(chart_data(ch, 'c')['x'], [1, 2, 3, 3, 2, 1]))
assert (np.array_equal(
chart_data(ch, 'c')['upper'], [0., 0., 4., 0., 0., 0.]))
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)
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)
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)
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)
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)
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)
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)
def test_multi_numeric_line(self):
"""Single line test"""
ch = chartify.Chart()
ch.plot.line(
self.data,
x_column='number1',
y_column='number2',
color_column='category1')
assert (np.array_equal(chart_data(ch, 'a')['number1'], [1., 2., 3.]))
assert (np.array_equal(chart_data(ch, 'a')['number2'], [5, 10, 0]))
assert (np.array_equal(chart_data(ch, 'b')['number1'], [1., 2., 3.]))
assert (np.array_equal(chart_data(ch, 'b')['number2'], [4, -3, -10]))
def test_lollipop_color_column(self):
sliced_data = self.data[self.data['category1'] == 'a']
ch = chartify.Chart(x_axis_type='categorical')
ch.plot.lollipop(
sliced_data,
categorical_columns='category2',
numeric_column='number',
color_column='category2')
assert (np.array_equal(
chart_color_mapper(ch).factors, ['1', '2', '3']))
assert (np.array_equal(
chart_color_mapper(ch).palette, ['#1f77b4', '#ff7f0e', '#2ca02c']))
def test_single_numeric_text(self):
"""Single line test"""
single_text = self.data[self.data['text'] == 'a']
ch = chartify.Chart()
ch.plot.text(
single_text,
x_column='number1',
y_column='number2',
text_column='text')
assert (np.array_equal(chart_data(ch, '')['number1'], [1., 2., 3.]))
assert (np.array_equal(chart_data(ch, '')['number2'], [5, 10, 0]))
assert (np.array_equal(chart_data(ch, '')['text'], ['a', 'a', 'a']))
def test_heatmap(self):
"""Area plot tests"""
ch = chartify.Chart(x_axis_type='categorical',
y_axis_type='categorical')
ch.plot.heatmap(self.data,
x_column='category1',
y_column='category2',
color_column='values')
assert (np.array_equal(
chart_data(ch, '')['category1'], ['a', 'a', 'b', 'b']))
assert (np.array_equal(
chart_data(ch, '')['category2'], ['1', '2', '1', '2']))
assert (np.array_equal(chart_data(ch, '')['values'], [1., 2., 3., 4.]))
def test_single_datetime_line(self):
"""Single line test"""
data = pd.DataFrame({
'number': [1, 10, -10, 0],
'datetimes':
['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04'],
})
ch = chartify.Chart(x_axis_type='datetime')
ch.plot.line(data, x_column='datetimes', y_column='number')
assert (np.array_equal(
chart_data(ch, '')['datetimes'],
pd.date_range('2017-01-01', '2017-01-04')))
assert (np.array_equal(chart_data(ch, '')['number'], [1, 10, -10, 0]))
def test_horizontal(self):
"""Horizontal test"""
sliced_data = self.data[self.data['category1'] == 'a']
for method_name in self.plot_methods:
ch = chartify.Chart(y_axis_type='categorical')
plot_method = getattr(ch.plot, method_name)
plot_method(
sliced_data,
categorical_columns='category2',
numeric_column='number')
assert (np.array_equal(
chart_data(ch, '')['factors'], ['2', '1', '3']))
assert (np.array_equal(chart_data(ch, '')['number'], [10, 5, 0]))
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))
def test_multi_series_zero_baseline_stacked(self):
"""Area plot tests"""
test_data = self.data
ch = chartify.Chart()
ch.plot.area(
test_data, 'x', 'upper', color_column='category', stacked=True)
assert (np.array_equal(chart_data(ch, 'a')['x'], [1, 2, 2, 1]))
assert (np.array_equal(
chart_data(ch, 'a')['upper'], [20., 30., 0., 0.]))
assert (np.array_equal(chart_data(ch, 'b')['x'], [1, 2, 2, 1]))
assert (np.array_equal(
chart_data(ch, 'b')['upper'], [22., 33., 30., 20.]))
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)
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)
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)
def test_categorical_axis_type_casting():
"""Categorical axis plotting breaks for non-str types.
Test that type casting is performed correctly"""
test_df = pd.DataFrame({
'categorical_strings': ['a', 'a', 'c', 'd'],
'categorical_integers': [1, 2, 3, 3],
'categorical_floats': [1.1, 2.1, 3.3, 4.4],
'numeric': [1, 2, 3, 4]
})
# Multi factor test
ch = chartify.Chart(blank_labels=True, x_axis_type='categorical')
ch.plot.bar(
test_df,
categorical_columns=['categorical_strings', 'categorical_integers'],
numeric_column='numeric')
# Single factor test
ch = chartify.Chart(blank_labels=True, y_axis_type='categorical')
ch.plot.bar(test_df, 'categorical_floats', 'numeric')
# Stacked bar test
ch = chartify.Chart(blank_labels=True, x_axis_type='categorical')
ch.plot.bar_stacked(test_df, 'categorical_strings', 'numeric',
'categorical_floats')
# Color test
ch = chartify.Chart(blank_labels=True, x_axis_type='categorical')
ch.plot.bar(
test_df,
categorical_columns=['categorical_strings', 'categorical_integers'],
numeric_column='numeric',
color_column='categorical_integers')
# Test that original data frame dtypes are not overwritten.
assert (test_df.dtypes['categorical_integers'] == 'int64')
assert (test_df.dtypes['categorical_floats'] == 'float64')
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)
def test_set_legend_location(self):
data = chartify.examples.example_data()
ch = chartify.Chart(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_legend_location('top_right')
assert (ch.figure.legend[0].location == 'top_right')
assert (ch.figure.legend[0].orientation == 'horizontal')
ch.set_legend_location('top_left', 'vertical')
assert (ch.figure.legend[0].location == 'top_left')
assert (ch.figure.legend[0].orientation == 'vertical')
ch.set_legend_location('outside_bottom')
ch = chartify.Chart(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_legend_location(None)
assert (ch.figure.legend[0].visible is False)
def main():
'''
This will load data from the Alaska State Department of Health and
Human Services and parse it into a standard format CSV
'''
filter_region = ['Statewide']
df=pdf.read_csv('./data/parsed.csv')
regions= ~df['Economic_Region'].isin(filter_region)
ch = chartify.Chart(x_axis_type='datetime', y_axis_type='linear')
ch.plot.scatter(data_frame=df_cases.loc[regions], x_column='Date_', y_column='daily_cases', color_column='Economic_Region',)
ch.show()
def setup(self):
data = chartify.examples.example_data()
data = data.sort_values(['date', 'fruit'])
color_order = data['fruit'].unique()
ch = chartify.Chart(x_axis_type='datetime')
ch.plot.scatter(data_frame=data,
x_column='date',
y_column='unit_price',
size_column='quantity',
color_column='fruit',
color_order=color_order)
self.chart = ch
self.data = data
self.color_order = color_order
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)
def test_multi_numeric_text(self):
"""Single line test"""
ch = chartify.Chart()
ch.plot.text(self.data,
x_column='number1',
y_column='number2',
text_column='text',
color_column='text')
assert (np.array_equal(chart_data(ch, 'a')['number1'], [1., 2., 3.]))
assert (np.array_equal(chart_data(ch, 'a')['number2'], [5, 10, 0]))
assert (np.array_equal(chart_data(ch, 'a')['text'], ['a', 'a', 'a']))
assert (np.array_equal(chart_data(ch, 'b')['number1'], [1., 2., 3.]))
assert (np.array_equal(chart_data(ch, 'b')['number2'], [4, -3, -10]))
assert (np.array_equal(chart_data(ch, 'b')['text'], ['b', 'b', 'b']))
def plotPublisher1000(fname='publishers_count.csv'):
'''
Author: Joshua Williams
Makes a plot of the number of games that belong to each developer in
the top 1000 ranking of our Dataset
:fname (str): The name of the file that contains the dataframe [must be a .csv file]
'''
import pandas as pd
import chartify as ch
assert isinstance(fname, str) and '.csv' in fname, "Input is not str name of a .csv file!"
# assert isinstance(fname2, str) and '.csv' in fname2, "Input is not str name of a .csv file!"
data = pd.read_csv(fname)
# data2 = pd.read_csd(fname2)
data = pd.DataFrame({'publisher': list(data.columns.values[1:11]),
'count': list(data.values[0][1:11])})
# Create a Chartify object for this plot
chart = ch.Chart(blank_labels=True, y_axis_type='categorical')
# Set Variables for plot
chart.set_title('Top 10 Game Publishers with Well Received Games on Steam')
chart.axes.set_xaxis_label('Number of Games Publisher has in Top 1000')
chart.axes.set_yaxis_label('Publishers')
chart.set_source_label('Steam Website')
chart.plot.bar(data_frame=data,
categorical_columns='publisher',
numeric_column='count',
color_column='publisher',
categorical_order_ascending=True)
chart.style.color_palette.reset_palette_order()
chart.plot.text(data_frame=data,
categorical_columns='publisher',
numeric_column='count',
text_column='count',
color_column='publisher',
categorical_order_ascending=True)
# Adjust the axis range to prevent clipping of the text labels.
chart.axes.set_xaxis_range(0, 20)
# Display Plot
chart.show()
def test_data(self):
data = chartify.examples.example_data()
data = data.sort_values(['date', 'fruit'])
color_order = data['fruit'].unique()
ch = chartify.Chart(x_axis_type='datetime')
ch.plot.scatter(data_frame=data,
x_column='date',
y_column='unit_price',
size_column='quantity',
color_column='fruit',
color_order=color_order)
assert (np.array_equal(
list(filter(lambda x: x['fruit'][0] == color_order[0],
ch.data))[0]['unit_price'],
data[data['fruit'] == color_order[0]]['unit_price'].values))
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))
