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99 of 100: Bar chart in matplotlib

At the beginning of the year I challenged myself to create all 100 visualizations using python and matplotlib from the 1 dataset,100 visualizations project and I am sharing with you the code for all the visualizations.

Note: Data Viz Project is copyright Ferdio and available under a Creative Commons Attribution – Non Commercial – No Derivatives 4.0 International license. I asked Ferdio and they told me they used a Design tool to create all the plots.

Collaborate

There are a ton of improvements that can be made on the code, so let me know in the comments any improvements you make and I will update the post accordingly!

To be improved: I am missing the gradient lines. I have an idea on how to do it, need time to implement it.

This is the original viz that we are trying to recreate in matplotlib:

Import the packages

We will need the following packages:

import matplotlib.pyplot as plt
from matplotlib.patches import FancyBboxPatch
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
import pandas as pd

Generate the data

We could actually go from numpy to matplotlib, but most data projects use pandas to transform the data, so I am using a pandas dataframe as the starting point.


color_dict = { 2022 : "#A54836", 2004: "#5375D4", }

xy_ticklabel_color, label_color, grid_color, datalabels_color ='#757C85',"#101628", "#C8C9C9", "#FFFFFF"

data = {
    "year": [2004, 2022, 2004, 2022, 2004, 2022],
    "countries" : [ "Denmark", "Denmark", "Norway", "Norway","Sweden", "Sweden",],
    "sites": [4,10,5,8,13,15]
}
df= pd.DataFrame(data)
indexyearcountriessites
02004Sweden13
12022Sweden15
22004Denmark4
32022Denmark10
42004Norway5
52022Norway8

We need to create the subtotals for each year so we use pandas groupby and then sort the data.

df['sub_total'] = df.groupby('countries')['sites'].transform('sum')
df = df.sort_values([ 'countries'], ascending=False ).reset_index(drop=True)
df['year_lbl'] ="'"+df['year'].astype(str).str[-2:].astype(str)
df['pct_change'] = df.groupby('countries', sort=False)['sites'].apply(
     lambda x: x.pct_change()).to_numpy()
#map the colors of a dict to a dataframe
df['color']= df.year.map(color_dict)
indexyearcountriessitessub_totalyear_lblpct_changecolor
02004Sweden1328’04NaN#5375D4
12022Sweden1528’220.153846#A54836
22004Norway513’04NaN#5375D4
32022Norway813’220.600000#A54836
42004Denmark414’04NaN#5375D4
52022Denmark1014’221.500000#A54836

Create a function to draw lines with gradient

#function to color a line
def multiColorLine(xstart, xend, ystart, yend, npoints, line_thickness, colors, ax, ):
    x = np.linspace(xstart, xend, npoints)
    y = [ystart]*(int(npoints/3)) + np.linspace(ystart,yend,int(npoints/3)).tolist() + [yend]*int(npoints/3) 

    cmap = LinearSegmentedColormap.from_list("", colors)
    norm = plt.Normalize(x.min(), x.max())
    line_colors = cmap(norm(x))

    ax.scatter(x, y, color=line_colors, s=line_thickness)

Define the variables

nr_countries = df.countries.nunique() 
countries = df.countries.unique()
years = df.year.unique()
lbl = df.year_lbl.unique()
colors= df.color.unique()
img = [plt.imread("flags/sw-sq.png"),plt.imread("flags/no-sq.png"), plt.imread("flags/de-sq.png")]

Plot the chart

fig, axes = plt.subplots(ncols = nr_countries, nrows = 1, figsize=(8,5),sharex=True, sharey=True, facecolor = "#FFFFFF", zorder= 1)
fig.tight_layout(pad=1.0)

y2004 = df[df.year==2004]['sites']
y2022 = df[df.year==2022]['sites']

for country, im, y04,y022, ax  in zip(countries, img, y2004,y2022, axes.ravel()):
    temp_df = df[df.countries==country]
    x = temp_df.year
    y = temp_df.sites
    pct = temp_df['pct_change'].max()
   
    ax.bar(x, height = y, width=7,color = colors)

    multiColorLine(2001, 2025, -0.5, -0.5, 900, 0.01, colors, ax)
    multiColorLine(2001, 2025, y04+0.3, y022+0.3, 900, 0.01, colors, ax)

    #add the grand totals at the top of the bars
    for bar, site in zip(ax.patches, y):
         ax.text(
            bar.get_x() + bar.get_width() / 2, 
            round(bar.get_height())+0.5,  #height
            site, ha="center", va="bottom", size= 12,
            color = label_color, weight= "bold", )

    image_box =  OffsetImage(im, zoom = 0.05) #container for the image
    ab = AnnotationBbox(image_box, (2013,1),  xybox= (2013, -4), frameon = False) # the x coordinate is the year axis
    ax.add_artist(ab)

    ####
    #  Round the edges 
    # #######     
    new_patches = []
    for patch in reversed(ax.patches):
        # print(bb.xmin, bb.ymin,abs(bb.width), abs(bb.height))
        bb = patch.get_bbox()
        color = patch.get_facecolor()
        p_bbox = FancyBboxPatch((bb.xmin, bb.ymin),
                                abs(bb.width), abs(bb.height),
                                boxstyle="round,pad=-0.0040,rounding_size=1",
                                ec="none", fc=color,
                                mutation_aspect=0.2
                                )
        patch.remove()
        new_patches.append(p_bbox)

    for patch in new_patches:
        ax.add_patch(patch)

    ##### 
    # end of round the edges
    #  #####

        
    ax.spines[['right', 'bottom','top','left']].set_visible(False)

    ax.set_ylim(-1,16)

    ax.xaxis.set_ticks(years, labels =lbl, color= label_color)
    ax.tick_params(axis='both', which='major',length=0, labelsize=11,colors= label_color)

    ax.set_yticklabels([])
    #add the symbol and pct labels
    ax.annotate(f'\u25B2\n{pct:.1%}', xy= (.5, .2),size= 12, color= label_color, weight = "bold", va = "center", ha = "center", xycoords="axes fraction" )
    ax.set_xlabel(country, size=14, color = label_color)
    ax.xaxis.set_label_coords(0.5,-0.25)

import IPython 
s = IPython.extract_module_locals()[1]['__vsc_ipynb_file__']
filename = str( s[s.find("dataviz\\")+len("dataviz\\"):s.rfind(".ipynb")])
fig.savefig(r'C:/Users/Ruth Pozuelo/Documents/SynologyDrive/Matplotlib-Labs/100 dataviz/0.Images/' + filename +'.png',
            bbox_inches = 'tight', facecolor=fig.get_facecolor(), transparent=False, dpi = 600)

The result:

99 of 100: Bar chart in matplotlib
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