def main():
""" PCA analysis on iris dataset.
Reads in data from specified directory, runs analysis, and saves figure.
"""
# Parse save directory from arguments
parser = argparse.ArgumentParser(description='Input / Output directories.')
parser.add_argument('inputdir', type=str, help='Input directory path.')
parser.add_argument('outdir', type=str, help='Output directory path.')
args = parser.parse_args()
INPUT_DIR = args.inputdir
OUTPUT_DIR = args.outdir
# Read data
cars = data.cars()
chart = alt.Chart(cars).mark_point().encode(
x='Horsepower',
y='Miles_per_Gallon',
color='Origin',
).interactive()
chart.save(OUTPUT_DIR)
print('PRINT Saved to: {}'.format(OUTPUT_DIR))
logging.info('Saved to: {}'.format(OUTPUT_DIR))
def make_interactive_chart():
'''
'''
pts = alt.selection_interval(encodings=['x', 'y'])
rect = alt.Chart(data.cars()).mark_point().encode(
x='Miles_per_Gallon:Q',
y='Horsepower:Q',
color=alt.condition(pts, 'Origin',
alt.value('lightgray'))).properties(selection=pts)
# scale=alt.Scale(scheme='greenblue'),
# legend=alt.Legend(title='Total Sample')
# )
# circ = rect.mark_point().encode(
# alt.ColorValue('grey'),
# alt.Size('count()',
# legend=alt.Legend(title='Sample in Selection')
# )
# ).transform_filter(
# pts
# )
return alt.vconcat(rect | rect.encode(x='Acceleration')).resolve_legend(
color="independent", size="independent")
def get(self, request, *args, **kwargs):
context = locals()
data3 = pd.DataFrame({
'a': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'],
'b': [28, 55, 43, 91, 81, 53, 19, 87, 52]
})
context['chart'] = alt.Chart(data3).mark_bar().encode(
x='a',
y='b'
).interactive()
source = data.cars()
context['chart2'] = alt.Chart(source).mark_circle().encode(
x='Horsepower',
y='Miles_per_Gallon',
color='Origin'
).interactive()
return render(request, self.template_name, context)
def make_static_chart1():
'''
'''
return alt.Chart(data=data.cars()).mark_circle(size=60).encode(
x='Horsepower:Q',
y='Miles_per_Gallon:Q',
color='Origin:N',
tooltip='Origin:N',
).interactive()
def test_cars_column_names():
cars = data.cars()
assert type(cars) is pd.DataFrame
assert tuple(cars.columns) == ('Acceleration', 'Cylinders', 'Displacement',
'Horsepower', 'Miles_per_Gallon', 'Name',
'Origin', 'Weight_in_lbs', 'Year')
cars = data.cars.raw()
assert type(cars) is bytes
def app():
st.title("Let's demo Streamlit basics")
st.sidebar.markdown("**Some tools we'll use:**")
st.sidebar.markdown("""
* Markdown
* Pandas
* Altair""")
st.markdown("""## Markdown and Pandas
I am writing Markdown but I can also show a pandas DataFrame.
""")
df_cars = data.cars()
st.write(df_cars.head())
st.markdown("""## Altair
And I can easily make interactive altair plots.
""")
brush = alt.selection_interval(encodings=['x', 'y'])
repeat_chart = alt.Chart(df_cars).mark_point().encode(
alt.X(alt.repeat('column'), type='quantitative'),
alt.Y('Miles_per_Gallon:Q'),
color=alt.condition(brush, 'Origin:N', alt.value('lightgray')),
opacity=alt.condition(
brush, alt.value(0.7), alt.value(0.1))).properties(
width=150, height=150).add_selection(brush).repeat(
column=['Weight_in_lbs', 'Acceleration', 'Horsepower'])
st.write(repeat_chart)
st.markdown("""## User Input
I can create a text input field to get input from the user.
""")
n = int(st.text_input("How many points do you want plotted:", '100'))
x = np.random.random(n) * 10
y = np.random.random(n) * 10
s = np.random.random(n)
df = pd.DataFrame({'x': x, 'y': y, 'size': s})
chart = alt.Chart(df, width=400,
height=400).mark_point().encode(
x='x',
y='y',
size=alt.Size('size', legend=None),
tooltip=['size']).interactive()
st.write(chart)
def test_cars_column_names():
cars = data.cars()
assert type(cars) is pd.DataFrame
assert sorted(cars.columns) == [
"Acceleration",
"Cylinders",
"Displacement",
"Horsepower",
"Miles_per_Gallon",
"Name",
"Origin",
"Weight_in_lbs",
"Year",
]
cars = data.cars.raw()
assert type(cars) is bytes
def app():
st.title("Streamlit demo for 202101")
st.markdown(""" ## Some section
This is great!
* bullet 1
* bullet 2
""")
st.sidebar.markdown('''This is the sidebar''')
df_cars = data.cars()
st.write(df_cars.head())
brush = alt.selection_interval(encodings=['x', 'y'])
repeat_chart = alt.Chart(df_cars).mark_point().encode(
alt.X(alt.repeat('column'), type='quantitative'),
alt.Y('Miles_per_Gallon:Q'),
color=alt.condition(brush, 'Origin:N', alt.value('lightgray')),
opacity=alt.condition(
brush, alt.value(0.7), alt.value(0.1))).properties(
width=150, height=150).add_selection(brush).repeat(
column=['Weight_in_lbs', 'Acceleration', 'Horsepower'])
st.write(repeat_chart)
st.markdown(""" ## User Input Demo
""")
n = int(st.text_input('How many point do you want', '100'))
x = np.random.random(n) * 10
y = np.random.random(n) * 10
s = np.random.random(n)
df = pd.DataFrame({'x': x, 'y': y, 'size': s})
chart = alt.Chart(df, width=400,
height=400).mark_point().encode(
x='x',
y='y',
size=alt.Size('size', legend=None),
tooltip=['size']).interactive()
st.write(chart)
def log_altair(step):
source = data.cars()
brush = alt.selection(type='interval')
points = alt.Chart(source).mark_point().encode(
x='Horsepower:Q',
y='Miles_per_Gallon:Q',
color=alt.condition(brush, 'Origin:N',
alt.value('lightgray'))).add_selection(brush)
bars = alt.Chart(source).mark_bar().encode(
y='Origin:N', color='Origin:N',
x='count(Origin):Q').transform_filter(brush)
chart = points & bars
tracking.log_altair_chart(name='altair_chart', figure=chart, step=step)
def _log_altair_figure():
source = data.cars()
brush = alt.selection(type='interval')
points = alt.Chart(source).mark_point().encode(
x='Horsepower:Q',
y='Miles_per_Gallon:Q',
color=alt.condition(brush, 'Origin:N',
alt.value('lightgray'))).add_selection(brush)
bars = alt.Chart(source).mark_bar().encode(
y='Origin:N', color='Origin:N',
x='count(Origin):Q').transform_filter(brush)
chart = points & bars
# Log altair figure as interactive chart in the experiment' artifacts tab.
log_chart(name='altair_chart', chart=chart)
def test_get_html_from_altair(self):
# given
source = data.cars()
chart = (
alt.Chart(source)
.mark_circle(size=60)
.encode(
x="Horsepower",
y="Miles_per_Gallon",
color="Origin",
tooltip=["Name", "Origin", "Horsepower", "Miles_per_Gallon"],
)
.interactive()
)
# when
result = get_html_content(chart)
# then
self.assertTrue(result.startswith("<!DOCTYPE html>\n<html>\n<head>\n <style>"))
def get_cars():
time.sleep(3) #Simulamos tiempo de carga
return data.cars()
if sub_topic == "Voice Activity Detection":
SG_activity.add_activity()
elif sub_topic == "Voice Activity Detection":
SG_gender.add_gender()
# IV. Data Visualization
elif topic == "Data Visualization":
sub_topic = st.sidebar.radio("Project", ["Dataset Explorer", "New-York Uber"])
page = st.sidebar.selectbox("Choose a page", ["Table", "Exploration"])
if sub_topic == "Dataset Explorer":
df = data.cars()
def visualize_data(df, x_axis, y_axis):
graph = alt.Chart(df).mark_circle(size=60).encode(
x=x_axis,
y=y_axis,
color='Origin',
tooltip=['Name', 'Origin', 'Horsepower', 'Miles_per_Gallon']).interactive()
st.write(graph)
if page == "Table":
st.header("Explore the raw table.")
st.write("Please select a page on the left.")
st.write(df)
elif page == "Exploration":
st.title("Data Exploration")
# streamlit run python_streamlit_dashboard.py
# *** IMPORTS ***
import streamlit as st
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LinearRegression
from vega_datasets import data as vds
# *** DATAFRAME ***
st.title('Cars DataFrame')
cars = vds.cars().dropna()
st.write(cars)
measures = ['Miles_per_Gallon', 'Cylinders', 'Displacement', 'Horsepower', 'Weight_in_lbs', 'Acceleration']
# *** SCATTER PLOT WITH DROPDOWN SELECTBOXES ***
st.title('Scatter Plot')
scatter_x = st.selectbox('x', measures)
scatter_y = st.selectbox('y', measures)
sns.regplot(x=scatter_x, y=scatter_y, data=cars)
st.pyplot()
# prediction model (i.e.-mpg at 7000 pounds)
st.title('Predict MPG')
weight = st.number_input('enter vehicle weight', min_value=1500, max_value=6000)
X = np.array(cars.Weight_in_lbs).reshape(-1,1)
"""
Selection Histogram
===================
This chart shows an example of using an interval selection to filter the
contents of an attached histogram, allowing the user to see the proportion
of items in each category within the selection.
"""
# category: interactive charts
import altair as alt
from vega_datasets import data
source = data.cars()
brush = alt.selection(type='interval')
points = alt.Chart().mark_point().encode(
x='Horsepower:Q',
y='Miles_per_Gallon:Q',
color=alt.condition(brush, 'Origin:N', alt.value('lightgray'))
).add_selection(
brush
)
bars = alt.Chart().mark_bar().encode(
y='Origin:N',
color='Origin:N',
x='count(Origin):Q'
).transform_filter(
brush
)
plt.plot(epochs, loss, 'r', label='Training Loss')
plt.plot(epochs, val_loss, 'b', label='Validation Loss')
plt.title('Training and validation loss')
plt.legend(loc=0)
plt.savefig('LossPlot.png',dpi=200)
plt.show()
!scp DeepLearn* *.png drive/My\ Drive/DataScience/PlantVillage
!scp *.csv drive/My\ Drive/DataScience/PlantVillage
# Commented out IPython magic to ensure Python compatibility.
# %load_ext google.colab.data_table
from vega_datasets import data
data.cars()
from IPython.display import display, Javascript
from google.colab.output import eval_js
from base64 import b64decode
def take_photo(filename='photo.jpg', quality=0.8):
js = Javascript('''
async function takePhoto(quality) {
const div = document.createElement('div');
const capture = document.createElement('button');
capture.textContent = 'Capture';
div.appendChild(capture);
const video = document.createElement('video');
video.style.display = 'block';
'y2': [1, 4, 2, 2, 3]})
stacked_area_plot = figure(plot_width=600, plot_height=300)
stacked_area_plot.varea_stack(['y1', 'y2'],
x='x',
color=('green', 'lightgreen'),
source=stacked_area_df)
show(stacked_area_plot)
In [68]:
# stacked_area_plot.varea_stack?
Scatter Plots
In [69]:
# vega data sets cars data
cars = vds.cars()
cars.tail()
Out[69]:
Acceleration Cylinders Displacement Horsepower Miles_per_Gallon Name Origin Weight_in_lbs Year
401 15.6 4 140.0 86.0 27.0 ford mustang gl USA 2790 1982-01-01
402 24.6 4 97.0 52.0 44.0 vw pickup Europe 2130 1982-01-01
403 11.6 4 135.0 84.0 32.0 dodge rampage USA 2295 1982-01-01
404 18.6 4 120.0 79.0 28.0 ford ranger USA 2625 1982-01-01
405 19.4 4 119.0 82.0 31.0 chevy s-10 USA 2720 1982-01-01
In [70]:
# scatter plot
# data
x_scatter = cars.Weight_in_lbs
y_scatter = cars.Miles_per_Gallon
'score': [28, 55, 43, 91, 81, 53, 19, 87, 52]
})
src2_x, src2_y = np.meshgrid(range(-5, 5), range(-5, 5))
src2_z = src2_x**2 + src2_y**2
# Convert this grid to columnar data expected by Altair
src2 = pd.DataFrame({
'a': src2_x.ravel(),
'b': src2_y.ravel(),
'c': src2_z.ravel()
})
tests = {
'test1-1': [
data.cars(), ('Q', 'Welcome to Jarvis!'),
('G', alt.Chart(data.cars()).mark_circle(opacity=0.9).encode(
x='Horsepower', y='Miles_per_Gallon').to_json()),
('Q', 'Can you draw the above graph with natural language?'), 2
],
'test1-2': [
data.cars(), ('Q', 'Welcome to Jarvis!'),
('G', alt.Chart(data.cars()).mark_circle(opacity=0.9).encode(
x='Horsepower', y='Miles_per_Gallon').to_json()),
('G',
alt.Chart(data.cars()).mark_circle(opacity=0.9 - 0.2 - 0.2).encode(
x='Horsepower', y='Miles_per_Gallon').to_json()),
('Q', 'Can you convert from the upper graph to the lower graph?'),
('P', 'scatterplot miles per gallon by horsepower'), 3
],
'test1-3': [
def SwitchExample(argument):
from vega_datasets import data
switcher = {"Airports": data.airports(), "Cars": data.cars()}
return switcher.get(argument, "Not found!")
from __future__ import annotations
import typing as t
from vega_datasets import data
if t.TYPE_CHECKING:
from pandas.core.frame import DataFrame
df: DataFrame = data.cars()
print(df.head())
print(df.describe())
print(df.columns)
grouped_df = df.groupby(by=["Year"]).agg(
{"Horsepower": ["max", "min", "mean", "std", "count"]})
print(grouped_df)
print("")
print("----------------------------------------")
print("")
df: DataFrame = data.iris()
print(df.head())
print(df.describe())
print(df.columns)
grouped_df = df.groupby(by=["species"]).agg({
"sepalLength": ["max", "min", "mean", "std", "count"],
"sepalWidth": ["max", "min", "mean", "std", "count"],
})
print(grouped_df)
def load_data():
df = data.cars()
return df
"""
Text over a Heatmap
-------------------
An example of a layered chart of text over a heatmap using the cars dataset.
"""
# category: other charts
import altair as alt
from vega_datasets import data
source = data.cars()
# Configure common options
base = alt.Chart(source)
scale = alt.Scale(paddingInner=0)
# Configure heatmap
heatmap = base.mark_rect().encode(alt.X('Cylinders:O', scale=scale),
alt.Y('Origin:O', scale=scale),
color='count()')
# Configure text
text = base.mark_text(baseline='middle').encode(x='Cylinders:O',
y='Origin:O',
text='count()',
color=alt.condition(
alt.datum['count_*'] > 100,
alt.value('black'),
alt.value('white')))
# Draw the chart
def __init__(self, path):
self.cars = data.cars()
self.path = path
def test_df():
test_data = data.cars()
return test_data
import altair as alt
from vega_datasets import data
print(data.cars())
cars = data.cars()
chart = alt.Chart(cars).mark_point().encode(
x='Horsepower',
y='Miles_per_Gallon',
color='Origin',
)
chart.save('chart.png')
def get_cars():
time.sleep(3) #estamos simulando la carga del dataset
return data.cars()
# Plot / Altair
# Use Altair to create plot specifications for the Vega card.
# ---
import altair
from vega_datasets import data
from h2o_wave import site, ui
spec = altair.Chart(data.cars()).mark_circle(size=60).encode(
x='Horsepower',
y='Miles_per_Gallon',
color='Origin',
tooltip=['Name', 'Origin', 'Horsepower', 'Miles_per_Gallon'
]).properties(width='container',
height='container').interactive().to_json()
page = site['/demo']
page['example'] = ui.vega_card(
box='1 1 4 5',
title='Altair Example',
specification=spec,
)
page.save()
def get_data_source():
return data.cars()
"""
REST API Resource Routing
http://flask-restplus.readthedocs.io
"""
from datetime import datetime
from flask import request
from flask_restplus import Resource, fields
from .security import require_auth
from . import api_rest
import altair
from vega_datasets import data
cars = data.cars()
class SecureResource(Resource):
""" Calls require_auth decorator on all requests """
method_decorators = [require_auth]
@api_rest.route('/resource/<string:resource_id>')
class ResourceOne(Resource):
""" Unsecure Resource Class: Inherit from Resource """
def get(self, resource_id):
timestamp = datetime.utcnow().isoformat()
return {'timestamp': timestamp}
def post(self, resource_id):
json_payload = request.json
"""
Violin Plot
-----------
This example shows how to make a Violin Plot using Altair's density transform.
"""
# category: other charts
import altair as alt
from vega_datasets import data
alt.Chart(data.cars()).transform_density(
'Miles_per_Gallon',
as_=['Miles_per_Gallon', 'density'],
extent=[5, 50],
groupby=['Origin']
).mark_area(orient='horizontal').encode(
y='Miles_per_Gallon:Q',
color='Origin:N',
x=alt.X(
'density:Q',
stack='center',
impute=None,
title=None,
axis=alt.Axis(labels=False, values=[0],grid=False, ticks=True),
),
column=alt.Column(
'Origin:N',
header=alt.Header(
titleOrient='bottom',
labelOrient='bottom',
labelPadding=0,
),