Line Graphs

Simple Line Graph

month_number = [1, 2, 3, 4, 5, 6, 7]
new_deaths = [213, 2729, 37718, 184064, 143119, 136073, 165003]
import matplotlib.pyplot as plt
plt.plot(month_number, new_deaths)
plt.show()

The plt.plot() function generates a line graph by default. All it needs is two arrays of data of the same length — these can be Python lists, pandas Series, NumPy arrays, etc. Above, we used two Python lists.

On the top left side of the graph, we see an "1e6" sign — this is scientific notation. Matplotlib changes to scientific notation if one value on the axis needs to be one million or greater. If we want to remove scientific notation, we can use the plt.ticklabel_format(axis, style) function.

Similary we can also add labels etc and customize our graph.

Customizing Line Graphs

import matplotlib.pyplot as plt

month_number = [1, 2, 3, 4, 5, 6, 7]
new_deaths = [213, 2729, 37718, 184064, 143119, 136073, 165003]
plt.plot(month_number,new_deaths)
plt.title('New Reported Deaths By Month (Globally)')
plt.xlabel('Month Number')
plt.ylabel('Number Of Deaths')
plt.show()

Customizing Pandas Dataframe

We can also import and prepare our panda dataframe for line graph

import pandas as pd
who_time_series = pd.read_csv('WHO_time_series.csv')
who_time_series['Date_reported'] = pd.to_datetime(who_time_series['Date_reported'])
print(who_time_series.head(5))
print(who_time_series.tail(5))
print(who_time_series.info())

We can filter out data and then make a graph

india = who_time_series[who_time_series['Country'] == 'India']

plt.plot(india['Date_reported'], india['New_cases'])
plt.title('India: New Reported Cases')
plt.xlabel('Date')
plt.ylabel('Number Of Cases')
plt.show()

Data trends

Multiple Plots

france = who_time_series[who_time_series['Country'] == 'France']
uk = who_time_series[who_time_series['Country'] == 'The United Kingdom']

plt.plot(france['Date_reported'], france['Cumulative_cases'],
         label='France')
plt.plot(uk['Date_reported'], uk['Cumulative_cases'],
         label='The UK')
plt.legend()
plt.show()

If we want 2 separate graphs we can use two show commands

plt.plot(france['Date_reported'], france['Cumulative_cases'])
plt.title('France')
plt.show()

plt.plot(uk['Date_reported'], uk['Cumulative_cases'])
plt.title('The UK')
plt.show()

The plt.xticks() function takes its name from the little lines on each axis to show unit lengths. These lines are called ticks, and the corresponding labels are tick labels. The x-axis has x-ticks, and the y-axis has y-ticks — there's also a plt.yticks() function.

import pandas as pd
import matplotlib.pyplot as plt

bike_sharing = pd.read_csv('day.csv')
bike_sharing['dteday'] = pd.to_datetime(bike_sharing['dteday'])


plt.plot(bike_sharing['dteday'], bike_sharing['casual'],
        label='Casual')
plt.plot(bike_sharing['dteday'], bike_sharing['registered'],
        label='Registered')
plt.xticks(rotation=30)
plt.ylabel('Bikes Rented')
plt.xlabel('Date')
plt.title('Bikes Rented: Casual vs. Registered')
plt.legend()
plt.show()