Sunday, January 29, 2017

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>

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