How to plot a time series with this dataframe?

SimoneA 0
Viewed 78

I want to plot this dataframe like a time series, a line for every country that every year increases or decreases according to 'count'. How can i do this?

        country  count
Year            
2005    Australia   2
2005    Austria     1
2005    Belgium     0
2005    Canada      4
2005    China       0
2006    Australia   3
2006    Austria     0
2006    Belgium     1
2006    Canada      5
2006    China       2
2007    Australia   5
2007    Austria     1
2007    Belgium     2
2007    Canada      6
2007    China       3

I'd like a thing like this: enter image description here

matplotlib time-series plot pandas python
4 Answers

You can use pd.pivot_table and df.plot for this:

df.pivot_table(index='Year', columns='country', values='count').plot(xticks=df.Year.unique())

Will return

enter image description here

T C Molenaar0

You can use seaborn.lineplot:

import seaborn as sns

df.Year = pd.to_datetime(df.Year)

sns.set(rc={'figure.figsize':(12, 8)}) # changed the figure size to avoid overlapping
sns.lineplot(data=df, x=df['Year'].dt.strftime('%Y'), # show only years with strftime
             y=df['count'], hue='country')

which givesenter image description here

Nuri Taş0

Using matplotlib (and seaborn styling):

Setup

import pandas as pd

data = {'Year': {0: 2005, 1: 2005, 2: 2005, 3: 2005, 4: 2005, 5: 2006,
                 6: 2006, 7: 2006, 8: 2006, 9: 2006, 10: 2007, 11: 2007, 
                 12: 2007, 13: 2007, 14: 2007}, 
        'country': {0: 'Australia', 1: 'Austria', 2: 'Belgium', 3: 'Canada', 
                    4: 'China', 5: 'Australia', 6: 'Austria', 7: 'Belgium', 
                    8: 'Canada', 9: 'China', 10: 'Australia', 11: 'Austria', 
                    12: 'Belgium', 13: 'Canada', 14: 'China'}, 
        'count': {0: 2, 1: 1, 2: 0, 3: 4, 4: 0, 5: 3, 6: 0, 7: 1, 8: 5, 9: 2, 
                  10: 5, 11: 1, 12: 2, 13: 6, 14: 3}}

df = pd.DataFrame(data)

Plot

import matplotlib.pyplot as plt
import seaborn as sns

sns.set_style("dark")

df_pivot = df.pivot(index='Year', columns='country', values='count')

fig, ax = plt.subplots(figsize=(16,10))
ax.plot(df_pivot)
ax.grid()
ax.set_ylabel('Count')
ax.set_xlabel('Years')
ax.set_xticks(df_pivot.index.unique())
ax.legend(df_pivot.columns, title='Country count', fontsize=16)
plt.show()

Result:

plot

As you can see from the answer by Nuri Taş, seaborn can do a lot of this "work" for you. But it is useful to understand what is actually being done.

ouroboros10

A pure pandas solution would be using pivot and plot

df = pd.DataFrame({'Year':[2005,2005,2005,2005,2005,
                        2006,2006,2006,2006,2006,
                       2007,2007,2007,2007,2007],
                 'country' : ['Australia', 'Austria','Belgium', 'Canada', 'China']*3,
                 'count':[2,1,0,4,0,3,0,1,5,2,5,1,2,6,3]})

df.pivot(index='Year', columns='country', values='count').plot(xticks=df['Year'].unique())

Resulting in:

enter image description here

TiTo0
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