amitrani6

import branca

# Create the map object
m = folium.Map(location=[48, -102], zoom_start=3)

# Create the colomap styles
colormap = branca.colormap.LinearColormap(
    vmin=us_data_gdf["Total_Pop_2021"].quantile(0.0),
    vmax=us_data_gdf["Total_Pop_2021"].quantile(1),
    colors=["red", "orange", "lightblue", "green", "darkgreen"],

amitrani6

m = folium.Map(location=[48, -102], zoom_start=3)

folium.Choropleth(
    geo_data=us_data_gdf,
    name="choropleth",
    data=us_data_gdf,
    columns= ["GEOID","Total_Pop_2021"],
    key_on="feature.properties.GEOID",
    fill_color="YlGn",
    fill_opacity=0.7,

amitrani6

m = folium.Map(location=[48, -102], zoom_start=3)

folium.Choropleth(
    geo_data=usmap_gdf,
    name="choropleth",
    data=all_states_census_df,
    columns= ["state_str","Total_Pop_2021"],
    key_on="feature.id",
    fill_color="YlGn",
    fill_opacity=0.7,

amitrani6

m = folium.Map(location=[48, -102], zoom_start=3)

folium.Choropleth(
    geo_data=usmap_json_with_id,
    name="choropleth",
    data=all_states_census_df,
    columns=["state_str", "Total_Pop_2021"],
    key_on="feature.id",
    fill_color="YlGn",
    fill_opacity=0.7,

amitrani6

# Import the folium library (only shown here once)
import folium

m = folium.Map(location=[48, -102], zoom_start=3)

folium.Choropleth(
    geo_data=usmap_json_no_id,
    name="choropleth",
    data=all_states_census_df,
    columns=["state", "Total_Pop_2021"],

amitrani6

# Create transformation columns of the adjusted close price

# Calculate the log of the adjusted close prices
inx_df['adj_close_log'] = np.log(inx_df['adj_close'])

# Calculate the square root of the adjusted close prices
inx_df['adj_close_sqrt'] = np.sqrt(inx_df['adj_close'])

# Calculate the cubed root of the adjusted close prices
inx_df['adj_close_cbrt'] = np.cbrt(inx_df['adj_close'])

amitrani6

# Plotly Growth Rate Chart For The Campbell Soup Company

# Calculate Growth Rate
cpb['growth_rate'] = cpb['Adj Close'].pct_change()
s_and_p['growth_rate'] = s_and_p['Adj Close'].pct_change()

fig = go.Figure()

# You can add multiple traces, in this case financial securities
fig.add_trace(go.Scatter(x=cpb.Date, y=cpb['growth_rate'],

amitrani6

# A Plotly OHLC Chart With A Rangeslider
# This code is adapted from the Plotly site
# Source: https://plot.ly/python/ohlc-charts/

# Import the necessary libraries
import pandas as pd
import numpy as np

# The section of the Plotly library needed
import plotly.graph_objects as go

amitrani6

# This code was adapted from Harrison Kinsley's tutorial
# Source: https://pythonprogramming.net/candlestick-ohlc-graph-matplotlib-tutorial/

# Import the necessary libraries
import pandas as pd
import numpy as np

from matplotlib import pyplot as plt
import matplotlib.dates as mdates
import matplotlib.ticker as mticker

amitrani6

# The code to generate a 3-D plot of Seinfeld and Curb Your Enthusiasm
# scripts with dimensions identified with Principal Component Analysis

import matplotlib.pyplot as plt

seinfeld_3d = transformed_data_3d[:num_seinfeld]
s3_x = [i[0] for i in seinfeld_3d]
s3_y = [i[1] for i in seinfeld_3d]
s3_z = [i[2] for i in seinfeld_3d]