<filename>examples/charts.py<gh_stars>1-10
# ---
# jupyter:
#   jupytext_format_version: '1.2'
#   kernelspec:
#     display_name: Python (jupyter virtualenv)
#     language: python
#     name: jupyter
#   language_info:
#     codemirror_mode:
#       name: ipython
#       version: 3
#     file_extension: .py
#     mimetype: text/x-python
#     name: python
#     nbconvert_exporter: python
#     pygments_lexer: ipython3
#     version: 3.6.5
# ---

# ## Maps
#
# Draw a CCG map, optionally with London separated out.

# + {"scrolled": false}
import numpy as np
import pandas as pd
from ebmdatalab import maps
import matplotlib.pyplot as plt
from importlib import reload

reload(maps)

plt.figure(figsize=(12, 8))
df = pd.read_json("ccg_list_size.json")
df.columns = [
    "date",
    "pct",
    "ccg_name",
    "total_list_size",
]  # The CCG column must be named 'pct'
df = df[df["date"] == "2018-10-01"]
plt = maps.ccg_map(
    df,
    title="CCG list sizes",
    map_year="2019",  # leave this unset to select current year
    column="total_list_size",
    separate_london=True,
    london_layout="horizontal",
)
plt.show()
# -

# You can also show the map as a cartogram where the CCGs are sized according to their patient population

plt = maps.ccg_map(
    df,
    title="CCG list sizes",
    column="total_list_size",
    cartogram=True,
    separate_london=False,
)
plt.show()

# ## Deciles
#
# Given a dataframe with a date column and a values column, compute
# percentiles for each date and plot them in a line chart.

# +
from ebmdatalab import charts
import matplotlib.gridspec as gridspec

import importlib

importlib.reload(charts)

# make a datafrom with a date column and a values column
df = pd.DataFrame(np.random.rand(1000, 1), columns=["val"])
months = pd.date_range("2018-01-01", periods=12, freq="M")
df["month"] = pd.to_datetime(np.random.choice(months, len(df)))

charts.deciles_chart(
    df,
    period_column="month",
    column="val",
    title="Random values",
    ylabel="n",
    show_outer_percentiles=True,
    show_legend=True,
)

# Now add a single line against the deciles
df_subject = pd.DataFrame(np.random.rand(12, 1), columns=["val"]) * 100
df_subject["month"] = months
df_subject.set_index("month")

plt.plot(df_subject["month"], df_subject["val"], "r--")
plt.show()

<filename>examples/charts.py<gh_stars>1-10
# ---
# jupyter:
#   jupytext_format_version: '1.2'
#   kernelspec:
#     display_name: Python (jupyter virtualenv)
#     language: python
#     name: jupyter
#   language_info:
#     codemirror_mode:
#       name: ipython
#       version: 3
#     file_extension: .py
#     mimetype: text/x-python
#     name: python
#     nbconvert_exporter: python
#     pygments_lexer: ipython3
#     version: 3.6.5
# ---

# ## Maps
#
# Draw a CCG map, optionally with London separated out.

# + {"scrolled": false}
import numpy as np
import pandas as pd
from ebmdatalab import maps
import matplotlib.pyplot as plt
from importlib import reload

reload(maps)

plt.figure(figsize=(12, 8))
df = pd.read_json("ccg_list_size.json")
df.columns = [
    "date",
    "pct",
    "ccg_name",
    "total_list_size",
]  # The CCG column must be named 'pct'
df = df[df["date"] == "2018-10-01"]
plt = maps.ccg_map(
    df,
    title="CCG list sizes",
    map_year="2019",  # leave this unset to select current year
    column="total_list_size",
    separate_london=True,
    london_layout="horizontal",
)
plt.show()
# -

# You can also show the map as a cartogram where the CCGs are sized according to their patient population

plt = maps.ccg_map(
    df,
    title="CCG list sizes",
    column="total_list_size",
    cartogram=True,
    separate_london=False,
)
plt.show()

# ## Deciles
#
# Given a dataframe with a date column and a values column, compute
# percentiles for each date and plot them in a line chart.

# +
from ebmdatalab import charts
import matplotlib.gridspec as gridspec

import importlib

importlib.reload(charts)

# make a datafrom with a date column and a values column
df = pd.DataFrame(np.random.rand(1000, 1), columns=["val"])
months = pd.date_range("2018-01-01", periods=12, freq="M")
df["month"] = pd.to_datetime(np.random.choice(months, len(df)))

charts.deciles_chart(
    df,
    period_column="month",
    column="val",
    title="Random values",
    ylabel="n",
    show_outer_percentiles=True,
    show_legend=True,
)

# Now add a single line against the deciles
df_subject = pd.DataFrame(np.random.rand(12, 1), columns=["val"]) * 100
df_subject["month"] = months
df_subject.set_index("month")

plt.plot(df_subject["month"], df_subject["val"], "r--")
plt.show()

# --- # jupyter: #   jupytext_format_version: '1.2' #   kernelspec: #     display_name: Python (jupyter virtualenv) #     language: python #     name: jupyter #   language_info: #     codemirror_mode: #       name: ipython #       version: 3 #     file_extension: .py #     mimetype: text/x-python #     name: python #     nbconvert_exporter: python #     pygments_lexer: ipython3 #     version: 3.6.5 # --- # ## Maps # # Draw a CCG map, optionally with London separated out. # + {"scrolled": false} # The CCG column must be named 'pct' # leave this unset to select current year # - # You can also show the map as a cartogram where the CCGs are sized according to their patient population # ## Deciles # # Given a dataframe with a date column and a values column, compute # percentiles for each date and plot them in a line chart. # + # make a datafrom with a date column and a values column # Now add a single line against the deciles