Libraries within Kent County Council

Importing Tools:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import matplotlib.patches as mpatches
from matplotlib.pyplot import figure

Reading in the data:

data = pd.read_csv('data\KCC_Libraries.csv')

Taking a quick look at the dataset:

data.head(5)

district	location	0-11	12-19	20-59	60+	other	total_borrowers	number_of_borrowed_items	computer_hours	number_of_visits	items_in_stock	staffing_costs	premises_costs	other_costs	total_costs	number_of_birth_and_death_registrations
0	Tunbridge Wells	Cranbrook	158	15	167	285	59	684	9866	167	2776	16850	17064	31317	16851	65232	3
1	Thanet	Margate	36	6	92	139	34	307	2502	376	2550	35477	35582	67085	35477	138144	24
2	Canterbury	Whitstable	112	8	139	259	51	569	4248	83	1337	25927	26095	40761	25927	92783	290
3	Shepway	Hythe	137	14	159	538	140	988	14108	485	3172	23627	23818	47713	23628	95159	399
4	Dover	Dover	118	17	172	338	63	708	7366	859	2765	51955	52156	149644	51956	253756	594

data.groupby(['district']).sum().sort_values('items_in_stock', ascending=False)

0-11	12-19	20-59	60+	other	total_borrowers	number_of_borrowed_items	computer_hours	number_of_visits	items_in_stock	staffing_costs	premises_costs	other_costs	total_costs	number_of_birth_and_death_registrations
district
Maidstone	635	132	723	874	271	2635	30654	1717	8512	208251	209116	394503	208253	811872	4166
Thanet	362	60	643	1237	308	2610	29631	2462	9416	152352	153105	422980	152354	728439	3385
Sevenoaks	773	144	795	946	222	2880	36524	1506	45765	138721	139708	251357	138723	529788	2557
Tonbridge & Malling	649	117	709	833	231	2539	32673	1529	8406	134760	135656	187196	134762	457614	2062
Canterbury	597	94	960	1582	280	3513	39616	2301	13325	132585	133704	331979	132587	598270	3716
Dover	280	66	491	1222	223	2282	27249	1966	6194	131149	131717	292365	131151	555233	1680
Swale	447	74	611	969	276	2377	33172	2155	10835	124258	124994	160675	124260	409929	1788
Tunbridge Wells	448	91	526	698	214	1977	25440	1621	6371	119155	119792	267544	119157	506493	2994
Shepway	345	55	446	943	266	2055	24546	2058	7324	117771	117842	227093	117773	462708	2188
Gravesham	263	109	423	565	160	1520	15697	2393	7386	117354	117845	166156	117356	401357	2161
Dartford	314	109	339	313	117	1192	12724	1863	5837	112125	112528	170247	112127	394902	2538
Ashford	345	96	451	781	150	1823	18957	1835	4684	95965	96510	246297	95967	438774	2810

Is the biggest library also the busiest?

big_libraries = data.sort_values("total_borrowers", ascending=False)[
    ["location", "total_borrowers", "number_of_visits"]
].head(20)

plt.figure(figsize=(8, 6), dpi=100)
plt.barh(
    big_libraries["location"],
    big_libraries["number_of_visits"],
    color="#F4B860",
    label="Total Visits",
)
plt.barh(
    big_libraries["location"],
    big_libraries["total_borrowers"],
    color="#C83E4D",
    label="Total Members",
)
plt.gca().spines["top"].set_visible(False)
plt.gca().spines["right"].set_visible(False)
plt.legend()
plt.ylabel("Location")
plt.suptitle("Is the library with the biggest membership the busiest?")
plt.figtext(
    0.9,
    -0.05,
    "Data Source: https://www.kent.gov.uk/",
    horizontalalignment="right",
    fontsize=6,
)
plt.savefig("Biggest_Busiest.jpg", bbox_inches="tight")
plt.show()

total_items = (
    data.groupby("district")
    .sum()
    .sort_values("items_in_stock", ascending=False)
    .reset_index()[["district", "items_in_stock", "number_of_visits"]]
)
total_items

plt.figure(dpi=100)
plt.barh(
    total_items.district,
    total_items.items_in_stock,
    color="#F4B860",
    label="Total Library Items",
)
plt.barh(
    total_items.district,
    total_items.number_of_visits,
    color="#C83E4D",
    label="Total Visits",
)
plt.gca().spines["top"].set_visible(False)
plt.gca().spines["right"].set_visible(False)
plt.legend()
plt.ylabel("Number of Items Available")
plt.suptitle("Number of items vs. Number of borrows")
plt.title("per District")
plt.figtext(
    0.9,
    -0.1,
    "Data Source: https://www.kent.gov.uk/",
    horizontalalignment="right",
    fontsize=6,
)
plt.xticks(rotation=60)
plt.savefig("Items_Borrows.jpg", bbox_inches="tight")
plt.show()

computers = data.sort_values("computer_hours", ascending=False).head(10)[
    ["location", "computer_hours"]
]

plt.figure(dpi=100)
plt.barh(computers["location"], computers["computer_hours"], color="#C83E4D")
plt.gca().spines["top"].set_visible(False)
plt.gca().spines["right"].set_visible(False)
plt.legend()
plt.ylabel("Location")
plt.xlabel("Total Hours")
plt.suptitle("Number Of Computer Hours Logged Per Disctrict")
plt.figtext(
    0.9,
    -0.05,
    "Data Source: https://www.kent.gov.uk/",
    horizontalalignment="right",
    fontsize=6,
)
plt.savefig("Computer_Hours.jpg", bbox_inches="tight")
plt.show()

ages_by_district = (
    data.groupby("district")
    .sum()
    .reset_index()
    .sort_values("60 ", ascending=True)[
        ["district", "0-11", "12-19", "20-59", "60 ", "other", "total_borrowers"]
    ]
)

plt.figure(dpi=100)
plt.barh(
    ages_by_district["district"], ages_by_district["0-11"], color="#6699CC", label="0-9"
)
plt.barh(
    ages_by_district["district"],
    ages_by_district["12-19"],
    color="#4A5859",
    label="12-19",
)
plt.barh(
    ages_by_district["district"],
    ages_by_district["20-59"],
    color="#A0EEC0",
    label="20-59",
)
plt.barh(
    ages_by_district["district"], ages_by_district["60 "], color="#F4B860", label="60 "
)
plt.barh(
    ages_by_district["district"],
    ages_by_district["other"],
    color="#C83E4D",
    label="other",
)
plt.gca().spines["top"].set_visible(False)
plt.gca().spines["right"].set_visible(False)
plt.legend()
plt.ylabel("Number Of Borrowers")
plt.suptitle("Number Of Borrowers Per District")
plt.figtext(
    0.9,
    -0.05,
    "Data Source: https://www.kent.gov.uk/",
    horizontalalignment="right",
    fontsize=6,
)
plt.savefig("District_Ages.png", bbox_inches="tight")
plt.show()