Plotnine flipbook

The ggplot library is a great way to make attractive graphics, but unfortunately it's an R library, so we can't use it with Python.

Instead, we'll use plotnine, which is an attempt to bring ggplot to Python.

In [1]:

from plotnine import *

from plotnine import *

In [2]:

import pandas as pd

import pandas as pd

In [21]:

df = pd.read_csv("christmas tree sales.csv")
df.head()

df = pd.read_csv("christmas tree sales.csv") df.head()

Out[21]:

	Year	Type of tree	Trees sold (millions)
0	2004	Real tree	27.1
1	2005	Real tree	32.8
2	2006	Real tree	28.6
3	2007	Real tree	31.3
4	2008	Real tree	28.2

In [22]:

ggplot(df)

ggplot(df)

Out[22]:

<ggplot: (318247485)>

In [23]:

ggplot(df) + aes(x='Year')

ggplot(df) + aes(x='Year')

Out[23]:

<ggplot: (318484294)>

In [24]:

(
    ggplot(df)
    + aes(x='Year')
)

( ggplot(df) + aes(x='Year') )

Out[24]:

<ggplot: (318513011)>

In [26]:

(
    ggplot(df)
    + aes(x='Year')
    + aes(y='Trees sold (millions)')
)

( ggplot(df) + aes(x='Year') + aes(y='Trees sold (millions)') )

Out[26]:

<ggplot: (318569960)>

In [28]:

(
    ggplot(df)
    + aes(x='Year')
    + aes(y='Trees sold (millions)')
    + geom_point()
)

( ggplot(df) + aes(x='Year') + aes(y='Trees sold (millions)') + geom_point() )

Out[28]:

<ggplot: (318323523)>

In [29]:

(
    ggplot(df)
    + aes(x='Year')
    + aes(y='Trees sold (millions)')
    + geom_point()
    + aes(color='Type of tree')
)

( ggplot(df) + aes(x='Year') + aes(y='Trees sold (millions)') + geom_point() + aes(color='Type of tree') )

Out[29]:

<ggplot: (318157629)>

In [ ]: