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pythondata-visualizationlinegraph

Effectively manage multiple line graphs(45 unique lines)

my groupby challenge I am trying to visualize using a line graph the food production of African Countries(45 from my data) over 10 years. After using the groupby function and unstack the plot comes out well but is not readable and the colour to distinguish each line is poor. From my lecturer's visualization, he used Wolfram.

How can I achieve this using Python or are there better alternatives to my approach?

Here is my code

#To make the legend readable we reduce the font size
from matplotlib.font_manager import FontProperties
fontP = FontProperties()
fontP.set_size('small')
fig, ax = plt.subplots(figsize = (20,16))


df1.groupby(['Year','Country',]).sum().unstack().plot(ax = ax)
ax.set_yscale("log")
ax.set_ylim(1,300000)

plt.ylabel('Year')
plt.xlabel('Total Value')
plt.title('Food Production in Africa over the Years')
plt.legend(title='Countries', bbox_to_anchor=(1.05, 1), loc='upper left', 
     prop=fontP)

My Lecturer's visualization using Wolfram

enter image description here

My attempt

enter image description here

Solution

  • Since the data were not available, I pseudo-created the population trends for each country in Africa from the most recent data, and then used ax.text(x,y,country) to create the data. It's not that crowded in your data, so I think it's applicable. This example is customized from the official reference.

    import pandas as pd
import numpy as np

df = pd.read_csv('./africa_t.csv', sep=',')

    enter image description here

    import matplotlib.pyplot as plt

fig, ax = plt.subplots(1, 1, figsize=(12, 14))

# These are the colors that will be used in the plot
ax.set_prop_cycle(color=[
    '#1f77b4', '#aec7e8', '#ff7f0e', '#ffbb78', '#2ca02c', '#98df8a',
    '#d62728', '#ff9896', '#9467bd', '#c5b0d5', '#8c564b', '#c49c94',
    '#e377c2', '#f7b6d2', '#7f7f7f', '#c7c7c7', '#bcbd22', '#dbdb8d',
    '#17becf', '#9edae5'])

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

ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()

fig.subplots_adjust(left=.06, right=.75, bottom=.02, top=.94)

ax.set_xticks(range(2011, 2020, 10))
ax.set_yticks(range(5000, 210000000, 5000000))
ax.xaxis.set_major_formatter('{x:.0f}')

ax.grid(True, 'major', 'y', ls='--', lw=.5, c='k', alpha=.3)

ax.tick_params(axis='both', which='both', labelsize=14,
               bottom=False, top=False, labelbottom=True,
               left=False, right=False, labelleft=True)

country = df.columns

for column in country:
    line, = ax.plot(df['Year'].to_list(), df[column].to_list(), lw=2.5)
    y_pos = df[column].to_list()[-1] - 0.5
    ax.text(2020.5, y_pos, column, fontsize=14, color=line.get_color())

plt.show()

    enter image description here