justinm0rgan

import pandas as pd
import plotly.graph_objects as go

# create race graph
def create_race_graph(df_filtered):
    # Select columns for bar graph
    race_columns = ["WtNHP", "BlNHP", "AsnNHP", "Hsp1P", "OthNHP", "Rc2plNHP"]
    race_labels = [
        "White",
        "Black or African American",

justinm0rgan

import pandas as pd
import plotly.graph_objects as go

# filter data for BK01
df_bk01 = df_acs_cdta_2021[df_acs_cdta_2021['GeoID'].str.contains('BK01')]

# select columns for bar graph
race_columns = ['WtNHP', 'BlNHP', 'AsnNHP', 'Hsp1P', 'OthNHP', 'Rc2plNHP']
race_labels = ['White', 'Black or African American', 'Asian', 'Hispanic or Latino', 
               'Some Other Race', 'Two or More Races']

justinm0rgan

import geopandas as gpd
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import plotly.io as pio

# convert to 4326 for plotly
gdf_parks.to_crs(4326, inplace=True)
gdf_cdta.to_crs(4326, inplace=True)

# define trace for parks layer

justinm0rgan

import geopandas as gpd
import contextily as ctx
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches

# convert to better crs for gpd
gdf_parks.to_crs(2263, inplace=True)
gdf_cdta.to_crs(2263, inplace=True)

# new plot

justinm0rgan

import geopandas as gpd
import plotly.graph_objects as go
import plotly.io as pio
import config


# convert to 4326 to view in plotly
gdf_cdta.to_crs(4326,inplace=True)

# define a trace

justinm0rgan

import geopandas as gpd

# import cdta
cdta_url="https://data.cityofnewyork.us/api/geospatial/xn3r-zk6y?accessType=DOWNLOAD&method=export&format=Shapefile"
gdf_cdta=gpd.read_file(cdta_url)

# add acre column in gdf_cdta
gdf_cdta['cdta_acres'] = round(gdf_cdta['shape_area'] / 43560)

# get info

justinm0rgan

import geopandas as gpd
import matplotlib.pyplot as plt
import contextily as ctx

# create a new plot using geopandas
fig, ax = plt.subplots(figsize=(10,10))
gdf_parks.plot(ax=ax,edgecolor='black', alpha=0.5)

# add any additional features to the plot
ax.set_title('NYC Park Properties', fontdict={"size":20})

justinm0rgan

import geopandas as gpd

# define url
parks_url = 'https://data.cityofnewyork.us/api/geospatial/enfh-gkve?method=export&format=Shapefile'
gdf_parks = gpd.read_file(parks_url) # import shapefile as gdf

# change crs
gdf_parks.to_crs(2263, inplace=True)

# preview gdf

justinm0rgan

# import packages
import matplotlib.pyplot as plt
import contextily as ctx

# plot map use helper function
def plot_column_from_dict_facecolor(gdf, col_label_cmap_dict, jstevens_col_mats, 
                                    alpha=0.85, font_size=14, save=False):
    """
    Takes in gdf, column label cmap dictionary, and color matrix
    Also uses plot_inset_legend helper function to create plots

justinm0rgan

# import necessary packages
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import matplotlib.colors as mcolors
import matplotlib.collections as mcollect

# helper function to plot inset legend
def plot_inset_legend(ax, color_matrix, x_label='X Label'):
    """
    Takes in axes from plot, color_matrix and optional x label arg