justinr922

elevationScale = st.slider('Elevation Scale', min_value = 0, max_value = 50)
radius = st.slider('Radius', min_value = 1, max_value = 50)

st.deck_gl_chart(layers = [{
                'data': location_data.iloc[0:num_of_points, :],
                'type': 'HexagonLayer',
                'elevationScale' : elevationScale,
                'radius' : radius,
                'extruded' : True,
                'opacity' : 1.0

justinr922

st.deck_gl_chart(layers = [{
                'data': location_df.drop('timestamp', axis=1),
                'type': 'HexagonLayer',
                'elevationScale' : 5,
                'radius' : 5,
                'extruded' : True,
                'opacity' : 1.0
                }])

justinr922

import pandas as pd #for creating/managing dataframes
import json as json #for reading Facebook's JSON files
import streamlit as st #for streamlit

justinr922

# Read in the file using the function we wrote
location_df= read_json('location_history.json')

#Call .head() to preview the dataframe
location_df.head()

justinr922

# We can apply Pandas.to_datetime() on the creation_timestamp
# column. The unit is 's' for "seconds since the epoch since that is 
# the current time format.

location_df['timestamp'] = pd.to_datetime(location_data['creation_timestamp'], unit='s')

#And drop the old column
location_df.drop('creation_timestamp', axis=1, inplace=True)

justinr922

# Each coordinate is in the form of a dictionary like so: 
#
# {'latitude': 34.3, 'longitude': -89.5}
#
# So we will use list compreshension to create a new column, 
# using dictionary.get(key) to access each value.
location_df['latitude'] = [value.get('latitude') for value in location_df['coordinate']]
location_df['longitude'] = [value.get('longitude') for value in location_df['coordinate']]

#And drop the old coordinate column, since we have gotten what we need out of it

justinr922

location_df = read_json('location_history.json')

#Preview first 5 rows of dataframe
location_df.head()

justinr922

import pandas as pd #for creating/managing dataframes
import json as json #for reading Facebook's JSON files

def read_json(path_to_json):
    # Opens the json file in memory
    with open(path_to_json, 'r', encoding='utf8') as file:
        # Reads the file into a string, and then uses jsons.loads (loadstring) 
        # to interpret the data
        data_file = json.loads(file.read())
    return pd.DataFrame(data_file['location_history'])