FRED Maps¶

This documentation serves as a quick start guide to FredMaps. The FRED Maps API enables developers to pull regional data or shape files.

Setup¶

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import geopandas as gpd
import matplotlib.pyplot as plt
from rich import print

import pyfredapi as pf

import geopandas as gpd import matplotlib.pyplot as plt from rich import print import pyfredapi as pf

Get geo-series info¶

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info = pf.get_geoseries_info(series_id="WIPCPI")
print(info)

info = pf.get_geoseries_info(series_id="WIPCPI") print(info)

Get Regional Data¶

get_geoseries returns a cross section of data that are published by region (e.g. state, county, MSA). Similar to the get_series function in FredSeries, get_geoseries returns a GeoseriesData object that contains the metadata for the series and the requested data.

In this example, we pull the U.S. state per capital personal income between 2010-2021.

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per_capita_personal_income_by_state = pf.get_geoseries(
    series_id="WIPCPI",
    start_date="2010-01-01",
    end_date="2021-01-01",
)

per_capita_personal_income_by_state = pf.get_geoseries( series_id="WIPCPI", start_date="2010-01-01", end_date="2021-01-01", )

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per_capita_personal_income_by_state_df = per_capita_personal_income_by_state.data

per_capita_personal_income_by_state_df = per_capita_personal_income_by_state.data

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per_capita_personal_income_by_state_df = per_capita_personal_income_by_state_df[
    per_capita_personal_income_by_state_df.date
    == per_capita_personal_income_by_state_df.date.max()
]

per_capita_personal_income_by_state_df = per_capita_personal_income_by_state_df[ per_capita_personal_income_by_state_df.date == per_capita_personal_income_by_state_df.date.max() ]

In [ ]:

per_capita_personal_income_by_state_df

per_capita_personal_income_by_state_df

Get Shape Files¶

get_shape_files returns the shape files as json in in Well-known text (WKT) format.

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state_shapes_files = pf.get_shape_files(shape="state")

state_shapes_files = pf.get_shape_files(shape="state")

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pf.get_shape_files(shape="state")

pf.get_shape_files(shape="state")

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state_shapes_files

state_shapes_files

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state_gdf = gpd.GeoDataFrame.from_features(state_shapes_files["features"])

state_gdf = gpd.GeoDataFrame.from_features(state_shapes_files["features"])

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joined_df = state_gdf[["geometry", "state_fips"]].merge(
    per_capita_personal_income_by_state_df,
    left_on=["state_fips"],
    right_on=["code"],
)

joined_df = state_gdf[["geometry", "state_fips"]].merge( per_capita_personal_income_by_state_df, left_on=["state_fips"], right_on=["code"], )

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joined_df.date.max()

joined_df.date.max()

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fig, ax = plt.subplots(figsize=(10, 10))

fig, ax = plt.subplots(figsize=(10, 10))

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from mpl_toolkits.axes_grid1 import make_axes_locatable

from mpl_toolkits.axes_grid1 import make_axes_locatable

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fig, ax = plt.subplots(1, 1)

fig, ax = plt.subplots(1, 1)

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divider = make_axes_locatable(ax)

divider = make_axes_locatable(ax)

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cax = divider.append_axes("bottom", size="5%", pad=0.1)

cax = divider.append_axes("bottom", size="5%", pad=0.1)

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joined_df[["geometry", "value"]].plot(
    column="value",
    ax=ax,
    legend=True,
    cax=cax,
    legend_kwds={
        "label": "Per Capita Personal Income 2022",
        "orientation": "horizontal",
    },
)

joined_df[["geometry", "value"]].plot( column="value", ax=ax, legend=True, cax=cax, legend_kwds={ "label": "Per Capita Personal Income 2022", "orientation": "horizontal", }, )

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fig

fig

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# Create a figure and axis
fig, ax = plt.subplots(figsize=(10, 10))

# Plot the GeoDataFrame
# Use the 'bea_regi_1' column to assign colors
joined_df.plot(ax=ax, column="value", legend=True, edgecolor="black", cmap="Set3")

# Optional: Customize the legend
ax.get_legend().set_bbox_to_anchor((1, 0.5))  # Adjust position
ax.get_legend().set_title("BEA Region")

# Create a figure and axis fig, ax = plt.subplots(figsize=(10, 10)) # Plot the GeoDataFrame # Use the 'bea_regi_1' column to assign colors joined_df.plot(ax=ax, column="value", legend=True, edgecolor="black", cmap="Set3") # Optional: Customize the legend ax.get_legend().set_bbox_to_anchor((1, 0.5)) # Adjust position ax.get_legend().set_title("BEA Region")

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# Optional: Customize the legend
ax.get_legend().set_bbox_to_anchor((1, 0.5))  # Adjust position
ax.get_legend().set_title("U.S. Per Capita Personal Income")

# Optional: Customize the legend ax.get_legend().set_bbox_to_anchor((1, 0.5)) # Adjust position ax.get_legend().set_title("U.S. Per Capita Personal Income")

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fig

fig

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fig.show()

fig.show()

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joined_df.plot(ax=ax, column="value", legend=True, edgecolor="black", cmap="Set1")

joined_df.plot(ax=ax, column="value", legend=True, edgecolor="black", cmap="Set1")

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plt.show()

plt.show()

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import matplotlib

print(matplotlib.get_backend())
matplotlib.use("agg")

import matplotlib print(matplotlib.get_backend()) matplotlib.use("agg")

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