Area Plot

%%capture
!pip3 install xlrd

import matplotlib.pyplot as plt
import pandas as pd

df_can = pd.read_excel(
    './data/ibm/canada.xlsx',
    sheet_name='Canada by Citizenship',
    skiprows=range(20),
    skipfooter=2
)

df_can.columns = list(map(lambda x: str(x), df_can.columns))

drops = [
    'AREA', 
    'REG', 
    'DEV', 
    'Type', 
    'Coverage'
]
df_can.drop(columns=drops, inplace=True)

columns = {
    'OdName': 'Country',
    'AreaName': 'Continent',
    'RegName': 'Region'
}

df_can.rename(columns=columns, inplace=True)

df_can.set_index('Country', inplace=True)

df_can['Total'] = df_can.sum(axis=1)

years = list(map(str, range(1980, 2014)))

df_can.sort_values('Total', ascending=False, axis=0, inplace=True)

df_top5 = df_can.head()
df_top5 = df_top5[years].transpose() 

df_top5.head()

df_top5.index = df_top5.index.map(int)
df_top5.plot(
    kind='area', 
    stacked=False,
    figsize=(20, 10)
)

plt.title('Immigration Trend of Top 5 Countries')
plt.ylabel('Number of Immigrants')
plt.xlabel('Years')

plt.show()

The unstacked plot has a default transparency (alpha value) at 0.5. We can modify this value by passing in the alpha parameter.

df_top5.plot(
    kind='area', 
    alpha=0.25,
    stacked=False,
    figsize=(20, 10),
)

plt.title('Immigration Trend of Top 5 Countries')
plt.ylabel('Number of Immigrants')
plt.xlabel('Years')

plt.show()

Two types of plotting

There are two styles/options of ploting with matplotlib. Plotting using the Artist layer and plotting using the scripting layer.

Option 1: Scripting layer (procedural method) - using matplotlib.pyplot as plt

You can use plt i.e. matplotlib.pyplot and add more elements by calling different methods procedurally; for example, plt.title(...) to add title or plt.xlabel(...) to add label to the x-axis.

# option 1: this is what we have been using so far
    df_top5.plot(kind='area', alpha=0.35, figsize=(20, 10)) 

    plt.title('Immigration trend of top 5 countries')
    plt.ylabel('Number of immigrants')
    plt.xlabel('Years')

Option 2: Artist layer (Object oriented method) - using an Axes instance from Matplotlib (preferred)

You can use an Axes instance of your current plot and store it in a variable (eg. ax). You can add more elements by calling methods with a little change in syntax (by adding *set_* to the previous methods). For example, use ax.set_title() instead of plt.title() to add title, or ax.set_xlabel() instead of plt.xlabel() to add label to the x-axis.

This option sometimes is more transparent and flexible to use for advanced plots.

# option 2: preferred option with more flexibility
    ax = df_top5.plot(kind='area', alpha=0.35, figsize=(20, 10))

    ax.set_title('Immigration Trend of Top 5 Countries')
    ax.set_ylabel('Number of Immigrants')
    ax.set_xlabel('Years')

Country	India	China	United Kingdom of Great Britain and Northern Ireland	Philippines	Pakistan
1980	8880	5123	22045	6051	978
1981	8670	6682	24796	5921	972
1982	8147	3308	20620	5249	1201
1983	7338	1863	10015	4562	900
1984	5704	1527	10170	3801	668