Data Visualisation - Line Plots

In [38]:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
%config InlineBackend.figure_format = 'retina'

Loading the date¶

In [3]:

unrate = pd.read_csv("UNRATE.csv")

In [4]:

unrate.head()

Out[4]:

	DATE	VALUE
0	1948-01-01	3.4
1	1948-02-01	3.8
2	1948-03-01	4.0
3	1948-04-01	3.9
4	1948-05-01	3.5

In [5]:

unrate.shape

Out[5]:

(824, 2)

Convert data string to datetime object¶

In [7]:

unrate["DATE"].dtype

Out[7]:

dtype('O')

In [8]:

unrate["DATE"] = pd.to_datetime(unrate["DATE"])

In [22]:

unrate["DATE"].dtype

Out[22]:

dtype('<M8[ns]')

Plotting¶

In [13]:

x_values = unrate["DATE"].iloc[0:12]

In [15]:

y_values = unrate["VALUE"].iloc[0:12]

In [39]:

plt.plot(x_values,y_values)

Out[39]:

[<matplotlib.lines.Line2D at 0x11c1a49b0>]

Fixing X-Axis Ticks¶

In [40]:

plt.plot(x_values,y_values)
plt.xticks(rotation = 90)

Out[40]:

(array([ 711127.,  711158.,  711187.,  711218.,  711248.,  711279.,
         711309.,  711340.,  711371.,  711401.,  711432.,  711462.]),
 <a list of 12 Text xticklabel objects>)

Adding axis label and title¶

In [41]:

plt.plot(x_values,y_values)
plt.xticks(rotation = 90)
plt.xlabel("Month")
plt.ylabel("Unemployment Rate")
plt.title("Monthly Unemployment Trends, 1948")

Out[41]:

<matplotlib.text.Text at 0x11ce78320>