In [1]:

    

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline


    

In [2]:

    

!wget http://www.metoffice.gov.uk/pub/data/weather/uk/climate/stationdata/southamptondata.txt


    

    




--2015-08-13 21:43:29--  http://www.metoffice.gov.uk/pub/data/weather/uk/climate/stationdata/southamptondata.txt
Resolving www.metoffice.gov.uk (www.metoffice.gov.uk)... 2.20.183.171, 2.20.183.152
Connecting to www.metoffice.gov.uk (www.metoffice.gov.uk)|2.20.183.171|:80... connected.
HTTP request sent, awaiting response... 200 OK
Length: 89374 (87K) [text/plain]
Saving to: ‘southamptondata.txt’

southamptondata.txt 100%[=====================>]  87.28K   487KB/s   in 0.2s   

2015-08-13 21:43:29 (487 KB/s) - ‘southamptondata.txt’ saved [89374/89374]

In [23]:

    

pd.read_csv?


    

In [42]:

    

dateparse = lambda x, y: pd.datetime.strptime(x+" "+y, '%Y %m')
remove_star = lambda x: x.replace("*", "")
df = pd.read_csv("southamptondata.txt", skiprows=[0, 1, 2, 3, 4, 6], sep=" ", skipinitialspace=True, 
                 na_values="---", parse_dates={'date': [0, 1]}, date_parser=dateparse, skipfooter=1, 
                 converters={2:remove_star, 3:remove_star, 4:remove_star, 5:remove_star})
df.head()


    

    
Out[42]:






  

    

      

      
date
      
tmax
      
tmin
      
af
      
rain
      
sun
    
  
  

    

      
0
      
1855-01-01 00:00:00
      
5.0
      
0.5
      
16
      
NaN
      
NaN
    
    

      
1
      
1855-02-01 00:00:00
      
2.4
      
-3.5
      
21
      
54.3
      
NaN
    
    

      
2
      
1855-03-01 00:00:00
      
7.9
      
0.8
      
12
      
61.3
      
NaN
    
    

      
3
      
1855-04-01 00:00:00
      
13.7
      
3.6
      
4
      
10.1
      
NaN
    
    

      
4
      
1855-05-01 00:00:00
      
15.0
      
5.6
      
2
      
60.0
      
NaN
    
  

In [35]:

    

df.info()


    

    




<class 'pandas.core.frame.DataFrame'>
Int64Index: 1743 entries, 0 to 1742
Data columns (total 6 columns):
date    1743 non-null datetime64[ns]
tmax    1732 non-null object
tmin    1736 non-null float64
af      1736 non-null object
rain    1742 non-null object
sun     1142 non-null object
dtypes: datetime64[ns](1), float64(1), object(4)
memory usage: 95.3+ KB

In [45]:

    

dates = df.date.values
rain = df.rain.values
tmax = df.tmax.values

plt.plot(dates, tmax)


    

    
Out[45]:





[<matplotlib.lines.Line2D at 0x7f99d60dc290>]