Using gridSpec to arrange multiple plots as Infographics using Python

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Matplotlib library with all its flair, has a specific function named GridSpec(). The basic purpose of this function is to set the geometry of the subplots in a figure. But this function with a bit tweak can be used to present multiple plots into a fig, and covert it into a infographic to demonstrate a story with a minimal amount of coding.

No additional library is required to install for using GridSpec(), it works very efficiently, in case we have the base matplotlib installed with python.

Code:

import matplotlib.pyplot as plt from matplotlib.gridspec import GridSpec data_all_emission = pd.read_csv("annualemission.csv") # considering only last ten records for better demonstration data_all_emission = data_all_emission.tail(10) # Create a 2x2 grid of plots fig, axes = plt.subplots(3, 2, constrained_layout=True, figsize=(20,10)) gs = GridSpec(3, 3, figure=fig) # Plot 1 - Cummulative graph containing all the information - multiple bars axes[0,0].set_title("Year Wise Emission", color="b") data_all_emission.plot(ax=axes[0,0], x="year", y=["nh3", "nox", "so2", "voc", "pm10", "pm25"], kind="bar") # Plot 2 - Cummulative graph containing all the information - stacked bar axes[0,1].set_title("Year Wise Emission", color="b") ax = axes[0,1] data_all_emission.plot(x="year", y="nox", kind="bar", ax=ax) data_all_emission.plot(x="year", y="nh3", kind="bar", ax=ax, color="C6") data_all_emission.plot(x="year", y="so2", kind="bar", ax=ax, color="C2") data_all_emission.plot(x="year", y="voc", kind="bar", ax=ax, color="C3") data_all_emission.plot(x="year", y="pm10", kind="bar", ax=ax, color="C4") data_all_emission.plot(x="year", y="pm25", kind="bar", ax=ax, color="C5") # Plot 3 - Cummulative graph containing all the information - multiple column with line axes[1,0].set_title("", color="b") data_all_emission['pm10'].plot(ax = axes[1,0],kind='bar', color='red') data_all_emission['pm25'].plot(ax = axes[1,0],kind='line', marker='*', color='black', ms=10) # Plot 4 - Pie chart depicting the various emissions contribution for the last year axes[1,1].set_title("", color="b") ax = axes[1,1] ax.axis('equal') data_oneyear_emission = [87.75399456, 28.73753448, 2.500908237, 33.64595918, 24.68887142, 19.91823327] ax.pie(data_oneyear_emission, labels = ["nh3", "nox", "so2", "voc", "pm10", "pm25"],autopct='%1.2f%%') # Plot 5 - Line chart depicting the various emissions contribution axes[2,0].set_title("", color="b") axes[2,0].scatter(data_all_emission["year"], data_all_emission["pm10"], c ="pink",linewidths = 2,marker ="s",edgecolor ="green",s = 50) axes[2,0].scatter(data_all_emission["year"], data_all_emission["pm25"], c ="yellow",linewidths = 2,marker ="^",edgecolor ="red",s = 200) # Plot 6 - Scatter chart depicting various emission axes[2,1].set_title("", color="b") axes[2,1].plot(data_all_emission["year"], data_all_emission["nox"], label = "Nox", linestyle="-") axes[2,1].plot(data_all_emission["year"], data_all_emission["so2"], label = "So2", linestyle="--") axes[2,1].plot(data_all_emission["year"], data_all_emission["pm10"], label = "PM10", linestyle="-.") axes[2,1].plot(data_all_emission["year"], data_all_emission["pm25"], label = "PM25", linestyle=":") # depict illustration fig.suptitle("Emissions in UK for the last 10 years") plt.show()

annualemission.csv file:

year	nh3	nox	so2	voc	pm10	pm25
1970		100	100	100	100	100
1971		99.60866	94.73848	100.2234	91.98165	88.64904
1972		99.24757	90.90876	97.98916	81.36547	77.83722
1973		105.1348	93.41885	103.5091	84.82977	79.18828
1974		98.61177	85.60625	99.59469	77.11811	73.4659
1975		95.78024	81.31908	97.78028	73.09667	64.93709
1976		97.4164	78.62831	100.1002	71.41608	63.7089
1977		97.98779	78.52168	103.2155	70.58038	63.24233
1978		99.4901	79.23909	104.9025	69.12694	60.81718
1979		103.0656	84.19333	107.6771	69.58273	62.19865
1980	100	98.25664	74.52168	104.6946	64.04883	55.17591
1981	99.64473	94.48094	67.67441	102.9414	61.43936	52.52082
1982	102.1541	94.39082	64.76253	103.6882	60.10658	51.66876
1983	102.4576	93.28414	59.77755	103.644	59.26957	49.91486
1984	103.4527	92.13409	57.51615	97.06149	52.983	45.03866
1985	102.5312	95.35628	57.84557	104.1628	58.49267	49.60396
1986	102.0716	98.30005	60.06391	108.1107	60.15754	51.62374
1987	103.205	100.8191	59.82018	109.9201	58.81281	47.46698
1988	101.1894	102.8586	58.89539	112.4591	57.67821	46.51254
1989	99.83748	104.6846	57.13024	114.2833	57.96487	45.92101
1990	102.5252	102.0491	54.90832	117.6629	55.32245	44.19974
1991	101.9632	99.60249	53.85406	115.0473	55.06404	44.22075
1992	98.03191	97.07307	52.44198	111.2425	51.90585	42.44986
1993	96.5465	91.67766	47.67251	105.5006	47.73274	39.24644
1994	98.11881	90.25446	43.36565	100.5698	45.08469	37.40968
1995	96.00009	86.45724	38.97931	93.29078	41.82557	34.35056
1996	98.99587	82.82792	33.17199	90.30169	41.66298	33.78143
1997	100.9373	76.19169	27.04854	86.20335	39.2198	32.1186
1998	101.2941	74.30948	26.90708	79.89956	37.16361	30.54426
1999	99.07644	70.2023	20.73919	72.12927	36.52284	30.23491
2000	96.96427	67.27746	19.83905	66.18495	34.57565	27.55024
2001	94.55035	66.14376	18.77597	63.42441	35.50784	27.43074
2002	93.54306	62.90013	16.90173	59.95043	31.20946	24.35101
2003	91.60464	61.86521	16.42902	55.57003	33.28321	24.5754
2004	91.5741	60.03181	13.93649	52.11064	30.90234	23.96429
2005	89.61648	59.29191	12.14972	48.79004	30.14321	23.66435
2006	88.22146	57.29529	11.44533	47.0125	29.24549	23.23405
2007	87.00332	54.92967	9.97534	45.38723	27.59104	22.04671