Exploratory Computing with Python

Developed by Mark Bakker

Notebook 8: Basics of Pandas for data analysis

In this Notebook we learn how to do basic data analysis with pandas.

In [2]:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt

Loading data with Pandas

Data is often stored in CSV files (Comma Separated Values, although the values can be separated by other things than commas). So far, we have loaded csv files with the np.loadtxt command. The loadtxt function has some basic functionality and works just fine, but when we have more elaborate data sets we want more sophisticated functionality. The most powerful and advanced package for data handling and analysis is called pandas, and is commonly imported as pd:

In [3]:
import pandas as pd

We will use only a few functions of the pandas package here. Full information on pandas can be found on the pandas website. Consider the following dataset, which is stored in the file transport.csv. It shows the percentage of transportation kilometers by car, bus or rail for four countries. The dataset has four columns.

country, car, bus, rail
some more explanations, yada yada yada
France, 86.1, 5.3, 8.6
Germany, 85.2, 7.1, 7.7
Netherlands, 86.4, 4.6, 9
United Kingdom, 88.2, 6.5, 5.3

This data file can be loaded with the read_csv function of the pandas package. The read_csv function has many options. We will use three of them here. The rows that need to be skipped are defined with the skiprows keyword (in this case row 1 with the yada yada text). The skipinitialspace keyword is set to True so that the column name ' car' is loaded without the initial space that is in the data file. And the index_col keyword is set to indicate that the names in column 0 can be used as an index to select a row.

In [4]:
tran = pd.read_csv('transport.csv', skiprows=[1], skipinitialspace=True, index_col=0)

pandas loads data into a DataFrame. A DataFrame is like an array, but has many additional features for data analysis. For starters, once you have loaded the data, you can print it to the screen

In [5]:
print(tran)
                 car  bus  rail
country                        
France          86.1  5.3   8.6
Germany         85.2  7.1   7.7
Netherlands     86.4  4.6   9.0
United Kingdom  88.2  6.5   5.3

When the DataFrame is large, you can still print it to the screen (pandas is smart enough not to show the entire DataFrame when it is very large), or you can simply print the first 5 lines of the DataFrame with the .head() function.

A better option is the display function to display a nicely formatted DataFrame to the screen.

In [6]:
display(tran)
car bus rail
country
France 86.1 5.3 8.6
Germany 85.2 7.1 7.7
Netherlands 86.4 4.6 9.0
United Kingdom 88.2 6.5 5.3

Basic DataFrame manipulation

The rows and columns of a DataFrame may have names, as for the tran DataFrame shown above. To find out which names are used for the columns, use the keys function, which is accessible with the dot syntax. You can loop through the names of the columns.

In [7]:
print('Names of columns:')
print(tran.keys())
for key in tran.keys():
    print(key)
Names of columns:
Index(['car', 'bus', 'rail'], dtype='object')
car
bus
rail

Each DataFrame may be indexed just like an array, by specifying the row and column number using the .iloc syntax (which stands for index location), where column 0 is the column labeled car (the column labeled as country was stored as an index when reading the csv file).

In [8]:
print(tran.iloc[0, 1])  # gives the bus data for France
print(tran.iloc[1, 0])  # gives the car data for Germany
print(tran.iloc[2, 2])  # gives the rail data for Netherlands
print(tran.iloc[3])     # all data for United Kindom
print(tran.iloc[:, 1])  # all data for bus
5.3
85.2
9.0
car     88.2
bus      6.5
rail     5.3
Name: United Kingdom, dtype: float64
country
France            5.3
Germany           7.1
Netherlands       4.6
United Kingdom    6.5
Name: bus, dtype: float64

Alternatively, and often more explicit, values in a DataFrame may be selected by specifying the indices by name, using the .loc syntax. This is a bit more typing but it is much more clearly what you are doing. The equivalent of the code cell above, but using indices by name is

In [9]:
print(tran.loc['France', 'bus'])
print(tran.loc['Germany', 'car'])
print(tran.loc['Netherlands', 'rail'])
print(tran.loc['United Kingdom'])
print(tran.loc[:, 'bus'])
5.3
85.2
9.0
car     88.2
bus      6.5
rail     5.3
Name: United Kingdom, dtype: float64
country
France            5.3
Germany           7.1
Netherlands       4.6
United Kingdom    6.5
Name: bus, dtype: float64

There are two alternative ways to access all the data in a column. First, you can simply specify the column name as an index, without having to use the .loc syntax. Second, the dot syntax may be used by typing .column_name, where column_name is the name of the column. Hence, the following three are equivalent

In [10]:
print(tran.loc[:, 'car'])  # all rows of 'car' column
print(tran['car'])        # 'car' column 
print(tran.car)
country
France            86.1
Germany           85.2
Netherlands       86.4
United Kingdom    88.2
Name: car, dtype: float64
country
France            86.1
Germany           85.2
Netherlands       86.4
United Kingdom    88.2
Name: car, dtype: float64
country
France            86.1
Germany           85.2
Netherlands       86.4
United Kingdom    88.2
Name: car, dtype: float64

If you want to access the data in a row, only the .loc notation works

In [11]:
tran.loc['France']
Out[11]:
car     86.1
bus      5.3
rail     8.6
Name: France, dtype: float64

numpy functions for DataFrames

DataFrame objects can often be treated as arrays, especially when they contain data. Most numpy functions work on DataFrame objects, but they can also be accessed with the dot syntax, like dataframe_name.function(). Simply type

tran.

in a code cell and then hit the [tab] key to see all the functions that are available (there are many). In the code cell below, we compute the maximum value of transportation by car, the country corresponding to the maximum value of transportation by car (in pandas this is idxmax rather than the argmax used in numpy), and the mean value of all transportation by car.

In [12]:
print('maximum car travel percentage:', tran.car.max())
print('country with maximum car travel percentage:', tran.car.idxmax())
print('mean car travel percentage:', tran.car.mean())
maximum car travel percentage: 88.2
country with maximum car travel percentage: United Kingdom
mean car travel percentage: 86.47500000000001

You can also find all values larger than a specified value, just like for arrays.

In [13]:
print('all rail travel above 8 percent:')
print(tran.rail[tran.rail > 8])
all rail travel above 8 percent:
country
France         8.6
Netherlands    9.0
Name: rail, dtype: float64

The code above identified France and Netherlands as the countries with more than 8% transport by rail, but the code returned a series with the country names and the value in the rail column. If you only want the names of the countries, you need to ask for the values of the index column

In [14]:
print(tran.index[tran.rail > 8].values)
['France' 'Netherlands']

Exercise 1. Average annual rainfall by country

The file annual_precip.csv contains the average yearly rainfall and total land area for all the countries in the world (well, there are some missing values); the data is available on the website of the world bank. Open the data file to see what it looks like (just click on it in the Files tab on the Jupyter Dashboard). Load the data with the read_csv function of pandas, making sure that the names of the countries can be used to select a row, and perform the following tasks:

  • Print the first 5 lines of the DataFrame to the screen with the .head() function.
  • Print the average annual rainfall for Panama and make sure to include the units.
  • Report the total land area of the Netherlands and make sure to include the units.
  • Report all countries with an average annual rainfall less than 200 mm/year
  • Report all countries with an average annual rainfall more than 2500 mm/year
  • Report all countries with an average annual rainfall that is within 50 mm/year of the average annual rainfall in the Netherlands
In [ ]:
 

Adding a column to a DataFrame

A column may be added to a DataFrame by simply specifying the name and values of the new column using the syntax DataFrame['newcolumn']=something. For example, let's add a column named public_transport, which is the sum of the bus and rail columns, and then find the country with the largest percentage of public transport

In [15]:
tran['public_transport'] = tran.bus + tran.rail
print('Country with largest percentage public transport:', tran.public_transport.idxmax())
Country with largest percentage public transport: Germany

Plotting DataFrames

You can plot the column or row of a DataFrame with matplotlib functions, as we have done in previous Notebooks, but pandas has also implemented its own, much more convenient, plotting functions (still based on matplotlib in the background, of course). The plotting capabilities of pandas use the dot syntax, like dataframe.plot(). All columns can be plotted simultaneously (note that the names appear on the axes and the legend is added automatically!).

In [16]:
tran.plot();  # plot all columns

You can also plot one column at a time. The style of the plot may be specified with the kind keyword (the default is 'line'). Check out tran.plot? for more options.

In [17]:
tran['bus'].plot(kind='bar');

Sorting DataFrames

DataFrames may be sorted with the .sort_values function. The keyword inplace=True replaces the values in the DataFrame with the new sorted values (when inplace=False a new DataFrame is returned, which you can store in a separate variable so that you have two datasets, one sorted and one unsorted). The sort_values function has several keyword arguments, including by which is either the name of one column to sort by or a list of columns so that data is sorted by the first column in the list and when values are equal they are sorted by the next column in the list. Another keyword is ascending, which you can use to specify whether to sort in ascending order (ascending=True, which is the default), or descending order (ascending=False)

In [18]:
print('Data sorted by car use:')
display(tran.sort_values(by='car'))
print('Data sorted by bus use:')
display(tran.sort_values(by='bus'))
Data sorted by car use:
car bus rail public_transport
country
Germany 85.2 7.1 7.7 14.8
France 86.1 5.3 8.6 13.9
Netherlands 86.4 4.6 9.0 13.6
United Kingdom 88.2 6.5 5.3 11.8
Data sorted by bus use:
car bus rail public_transport
country
Netherlands 86.4 4.6 9.0 13.6
France 86.1 5.3 8.6 13.9
United Kingdom 88.2 6.5 5.3 11.8
Germany 85.2 7.1 7.7 14.8

Renaming columns

Sometimes (quite often, really), the names of columns in a dataset are not very convenient (long, including spaces, etc.). For the example of the transportation data, the columns have convenient names, but let's change them for demonstration purposes. You can rename columns inplace, and you can change as many columns as you want. The old and new names are specified with a Python dictionary. A dictionary is a very useful data type. It is specified between braces {}, and links a word in the dictionary to a value. The value can be anything. You can then use the word in the dictionary as the index, just like you would look up a word in an paper dictionary.

In [19]:
firstdictionary = {'goals': 20, 'city': 'Delft'}
print(firstdictionary['goals'])
print(firstdictionary['city'])
20
Delft

Much more on Python dictionaries can be found, for example, here. Let's continue with renaming two of the columns of the tran DataFrame:

In [20]:
tran.rename(columns={'bus': 'BUS', 
                     'rail': 'train'}, inplace=True)
display(tran)
car BUS train public_transport
country
France 86.1 5.3 8.6 13.9
Germany 85.2 7.1 7.7 14.8
Netherlands 86.4 4.6 9.0 13.6
United Kingdom 88.2 6.5 5.3 11.8

The index column, with the countries, is now called 'country', but we can rename that too, for example to 'somewhere in Europe', with the following syntax

In [21]:
tran.index.names = ['somewhere in Europe']
display(tran)
car BUS train public_transport
somewhere in Europe
France 86.1 5.3 8.6 13.9
Germany 85.2 7.1 7.7 14.8
Netherlands 86.4 4.6 9.0 13.6
United Kingdom 88.2 6.5 5.3 11.8

Exercise 2. Average annual rainfall by country continued

Continue with the average yearly rainfall and total land area for all the countries in the world and perform the following tasks:

  • Add a new column that stores the total average annual freshwater influx in km$^3$/year for each country. Make sure you convert your units correctly.
  • Sort the data on the total average annual freshwater influx in ascending order and report the 5 countries with the largest annual freshwater influx using the iloc syntax.
  • Make a bar graph of the 10 countries with the largest annual freshwater influx.
In [ ]:
 

Time series data

In time series data, one of the columns represents dates, sometimes including times, together referred to as datetimes. pandas can be used to read csv files where one of the columns includes datetime data. You need to tell pandas which column contains datetime values and pandas will try to convert that column to datetime objects. Datetime objects are very convenient as specifics of the datetime object may be assessed with the dot syntax: .year returns the year, .month returns the month, etc.

For example, consider the following data stored in the file timeseries1.dat

date, conc
2014-04-01, 0.19
2014-04-02, 0.23
2014-04-03, 0.32
2014-04-04, 0.29

The file may be read with read_csv using the keyword parse_dates=[0] so that column number 0 is converted to datetimes

In [22]:
data = pd.read_csv('timeseries1.dat', parse_dates=[0], skipinitialspace=True)
display(data)
date conc
0 2014-04-01 0.19
1 2014-04-02 0.23
2 2014-04-03 0.32
3 2014-04-04 0.29
4 2014-04-05 0.32

The rows of the DataFrame data are numbered, as we have not told pandas what column to use as the index of the rows (we will do that later). The first column of the DataFrame data has datetime values. We can access, for example, the year, month, and day with the dot syntax

In [23]:
print('datetime of row 0:', data.iloc[0, 0])
print('year  of row 0:', data.iloc[0, 0].year)
print('month of row 0:', data.iloc[0, 0].month)
print('day   of row 0:', data.iloc[0, 0].day)
datetime of row 0: 2014-04-01 00:00:00
year  of row 0: 2014
month of row 0: 4
day   of row 0: 1

You can get part of the date from an entire column (so for all rows) using the .dt syntax

In [24]:
data.date.dt.day # day for entire date column
Out[24]:
0    1
1    2
2    3
3    4
4    5
Name: date, dtype: int64

Time series data may also contain the time in addition to the date. For example, the data of the file timeseries2.dat, shown below, contains the day and time. You can access the hour or minutes, but also the time of a row of the DataFrame with the .time() function.

date, conc
2014-04-01 12:00:00, 0.19
2014-04-01 13:00:00, 0.20
2014-04-01 14:00:00, 0.23
2014-04-01 15:00:00, 0.21

In [25]:
data2 = pd.read_csv('timeseries2.dat', parse_dates=[0], skipinitialspace=True)
display(data2)
print('hour  of row 0:', data2.iloc[0, 0].hour)
print('minute  of row 0:', data2.iloc[0, 0].minute)
print('time of row 0:', data2.iloc[0, 0].time())
date conc
0 2014-04-01 12:00:00 0.19
1 2014-04-01 13:00:00 0.20
2 2014-04-01 14:00:00 0.23
3 2014-04-01 15:00:00 0.21
hour  of row 0: 12
minute  of row 0: 0
time of row 0: 12:00:00

Setting values based on a condition

Values of a column may be changed based on a condition. For example, all values of the concentration above 0.2 may be set to 0.2 with the following syntax

In [26]:
data2.loc[data2.conc>0.2, 'conc'] = 0.2
display(data2)
date conc
0 2014-04-01 12:00:00 0.19
1 2014-04-01 13:00:00 0.20
2 2014-04-01 14:00:00 0.20
3 2014-04-01 15:00:00 0.20

Exercise 3, Load and plot daily rainfall

Rainfall data for the Netherlands may be obtained from the website of the Royal Dutch Meteorological Society KNMI . Daily rainfall for the weather station Rotterdam in 2012 is stored in the file rotterdam_rainfall_2012.txt. First open the file in a text editor to see what the file looks like. At the top of the file, an explanation is given of the data in the file. Read this. Load the data file with the read_csv function of pandas. Use the keyword skiprows to skip all rows except for the row with the names of the columns. Use the keyword parse_dates to give either the name or number of the column that needs to be converted to a datetime. Don't forget the skipinitialspace keyword, else the names of the columns may start with a bunch of spaces. Perform the following tasks:

  • Convert the rainfall data to mm/d.
  • Some rainfall values in the dataset may be -1 (read the header of the file to learn why); set all rainfall values that are less than zero to zero.
  • Use the plot function of pandas to create a line plot of the daily rainfall with the number of the day (so not the date) along the horizontal axis.
  • Use matplotlib functions to add labels to the axes and set the limits along the horizontal axis from 0 to 365.
  • Determine the maximum daily rainfall and the date of the maximum daily rainfall and print them to the screen.
In [ ]:
 

Exercise 4, Compute monthly rainfall from daily rainfall

In this exercise we are going to compute the total monthly rainfall for 2012 in the City of Rotterdam using the daily rainfall measurements we loaded in the previous Exercise. Later on in this Notebook we learn convenient functions from pandas to do this, but here we are going to do this with a loop. Create an array of 12 zeros to store the monthly totals and loop through all the days in 2012 to compute the total rainfall for each month. The month associated with each row of the DataFrame may be obtained with the .month syntax, as shown above. Print the monthly totals (in mm/month) to the screen and create a bar graph of the total monthly rainfall (in mm/month) vs. the month using the plt.bar function of matplotlib.

In [ ]:
 

Date times as index

The datetime of a dataset may also be used as the index of a DataFrame by specifying the column with the dates as the column to use for an index with the index_col keyword. Note that datetimes are given as year-month-day, so 2012-04-01 means April 1, 2012.

In [27]:
data = pd.read_csv('timeseries1.dat', parse_dates=[0], index_col=0)
display(data)
print('data on April 1:', data.loc['2014-04-01'])
print('data on April 2:', data.loc['2014-04-02'])
conc
date
2014-04-01 0.19
2014-04-02 0.23
2014-04-03 0.32
2014-04-04 0.29
2014-04-05 0.32
data on April 1:  conc    0.19
Name: 2014-04-01 00:00:00, dtype: float64
data on April 2:  conc    0.23
Name: 2014-04-02 00:00:00, dtype: float64

Resampling

DataFrames have a very powerful feature called resampling. Downsampling refers to going from high frequency to low frequency. For example, going from daily data to monthly data. Upsampling refers to going from low frequency to high frequency. For example going from monthly data to daily data. For both upsampling and downsampling, you need to tell pandas how to perform the resampling. Here we discuss downsampling, where we compute monthly totals from daily values. First we load the daily rainfall in Rotterdam in 2012 from the file rotterdam_rainfall_2012.txt and specify the dates as the index (this is the column labeled as YYYYMMDD). We resample the rain to monthly totals using the resample function. You have to tell the resample function to what frequency it needs to resample. Common ones are 'A' for yearly, 'M' for monthly, 'W' for weekly, 'D' for daily, and 'H' for hourly, but there are many other ones (see here). The keyword argument kind is used to tell pandas where to assign the computed values to. You can assign the computed value to the last day of the period, or the first day, or to the entire period (in this case the entire month). The latter is done by specifying kind='period', which is what we will do here. Finally, you need to specify how to resample. This is done by adding a numpy function at the end of the resample statement, like

dataframe.resample(...).npfunc()

where npfunc can be any numpy function like mean for the mean (that is the default), sum for the total, min, max, etc. Calculating the monthly totals and making a bar graph can now be done with pandas as follows.

In [28]:
rain = pd.read_csv('rotterdam_rainfall_2012.txt', skiprows=9,
                parse_dates=['YYYYMMDD'], index_col='YYYYMMDD',
                skipinitialspace=True)
rain.RH[rain.RH<0] = 0  # remove negative values
rain.RH = rain.RH * 0.1 # convert to mm/day
monthlyrain = rain.RH.resample('M', kind='period').sum()
display(monthlyrain)
monthlyrain.plot(kind='bar')
plt.ylabel('mm/month')
plt.xlabel('month');
YYYYMMDD
2012-01     83.0
2012-02     24.3
2012-03     21.9
2012-04     57.6
2012-05     76.5
2012-06    119.0
2012-07    121.6
2012-08     93.4
2012-09     52.0
2012-10    132.6
2012-11     63.3
2012-12    149.5
Freq: M, Name: RH, dtype: float64

Exercise 5, Resample weather data

The file rotterdam_weather_2000_2010.txt contains daily weather data at the weather station Rotterdam for the period 2000-2010 (again from the KNMI). Open the data file in an editor to see what is in it. Perform the following tasks:

  • Load the data making sure the dates are used as index.
  • Convert the rain and evaporation to mm/day, and the temperature to degrees Celcius.
  • Set any negative rainfall (explained in the file) to zero.
  • Compute total yearly rainfall, total yearly evaporation, and mean yearly temperature.
  • Make a line plot of the yearly rainfall, yearly evaporation, and mean yearly temperature using the plot function of pandas. Plot the mean temperature on the secondary $y$-axis (use the help function to find out how).
In [ ]:
 

Rolling mean or rolling total

The resample method resamples to, for example, weeks, one week at a time. The rolling method performs a similar computation for a moving window, where the first argument is the length of the moving window. For example, a 30 day rolling total rainfall first computes the total rainfall in the first 30 days, from day 0 till day 30, then from day 1 till day 31, from day 2 till 32, etc. The value can be assigned to the end of the rolling period, or to the center of the rolling period (by setting center=True). The monthly total rainfall and 30-day rolling total are compared in the figure below.

In [29]:
plt.figure(figsize=(12, 4))
plt.subplot(121)
monthlyrain.plot(kind='bar')
plt.xlabel('2012')
plt.ylabel('total monthly rainfall (mm)')
plt.subplot(122)
rain.RH.rolling(30, center=True).sum().plot()
plt.xlabel('2012')
plt.ylabel('total 30-day rainfall (mm)');

Solutions to the exercises

In [28]:
rain = pd.read_csv('annual_precip.csv', skiprows=2, index_col=0)
#
print('First five lines of rain dataset:')
display(rain.head())
#
print()
print('Average annual rainfall in Panama is',rain.loc['Panama','precip'],'mm/year')
#
print()
print('Land area of the Netherlands is', rain.loc['Netherlands','area'], 'thousand km^2')
#
print()
print('Countries where average rainfall is below 200 mm/year')
display(rain[ rain.precip < 200 ])
#
print()
print('Countries where average rainfall is above 2500 mm/year')
display(rain[ rain.precip > 2500 ])
#
print()
print('Countries with almost the same rainfall as Netherlands')
display(rain[abs(rain.loc['Netherlands','precip'] - rain.precip) < 50])
First five lines of rain dataset:
precip area
country
Afghanistan 327.0 652.2
Albania 1485.0 27.4
Algeria 89.0 2381.7
American Samoa NaN 0.2
Andorra NaN 0.5
Average annual rainfall in Panama is 2692.0 mm/year

Land area of the Netherlands is 33.7 thousand km^2

Countries where average rainfall is below 200 mm/year
precip area
country
Algeria 89.0 2381.7
Bahrain 83.0 0.8
Egypt, Arab Rep. 51.0 995.5
Jordan 111.0 88.8
Kuwait 121.0 17.8
Libya 56.0 1759.5
Mauritania 92.0 1030.7
Niger 151.0 1266.7
Oman 125.0 309.5
Qatar 74.0 11.6
Saudi Arabia 59.0 2149.7
Turkmenistan 161.0 469.9
United Arab Emirates 78.0 83.6
Yemen, Rep. 167.0 528.0
Countries where average rainfall is above 2500 mm/year
precip area
country
Bangladesh 2666.0 130.2
Brunei Darussalam 2722.0 5.3
Colombia 2612.0 1109.5
Costa Rica 2926.0 51.1
Fiji 2592.0 18.3
Indonesia 2702.0 1811.6
Malaysia 2875.0 328.6
Panama 2692.0 74.3
Papua New Guinea 3142.0 452.9
Sao Tome and Principe 3200.0 1.0
Sierra Leone 2526.0 71.6
Solomon Islands 3028.0 28.0
Countries with almost the same rainfall as Netherlands
precip area
country
Burkina Faso 748.0 273.6
Lesotho 788.0 30.4
Mexico 752.0 1944.0
Netherlands 778.0 33.7
Slovak Republic 824.0 48.1
Swaziland 788.0 17.2
In [29]:
rain['totalq'] = rain.precip * rain.area * 1e-3
#
print('Five countries with largest annual influx:')
rain.sort_values(by='totalq', ascending=False, inplace=True)
display(rain[:5])
#
rain.totalq[:10].plot(kind='bar');
Five countries with largest annual influx:
precip area totalq
country
Brazil 1782.0 8459.4 15074.6508
Russian Federation 460.0 16376.9 7533.3740
United States 715.0 9147.4 6540.3910
China 645.0 9327.5 6016.2375
Indonesia 2702.0 1811.6 4894.9432
In [30]:
rain = pd.read_csv('rotterdam_rainfall_2012.txt', skiprows=9,
                parse_dates=['YYYYMMDD'], skipinitialspace=True)
# convert to mm/d
rain.iloc[:,2] = rain.iloc[:,2] * 0.1
# set negative values to zero
rain.loc[rain.RH < 0, 'RH'] = 0
rain.RH.plot()
plt.xlabel('day')
plt.ylabel('daily rainfall (mm/day)')
plt.xlim(0, 365)
print('Maximum daily rainfall', rain.RH.max())
print('Date of maximum daily rainfall', rain.YYYYMMDD[rain.RH.idxmax()])
Maximum daily rainfall 22.400000000000002
Date of maximum daily rainfall 2012-12-22 00:00:00
In [31]:
monthlyrain = np.zeros(12)
for i in range(len(rain)):
    month = rain.iloc[i,1].month
    monthlyrain[month - 1] += rain.iloc[i, 2]
print(monthlyrain)
#
plt.bar(np.arange(12), monthlyrain, width=0.8)
plt.xlabel('month')
plt.ylabel('monthly rainfall (mm/month)')
plt.xticks(np.arange(12), ['J', 'F', 'M', 'A', 'M', 'J', 'J', 'A', 'S', 'O', 'N', 'D']);
[ 83.   24.3  21.9  57.6  76.5 119.  121.6  93.4  52.  132.6  63.3 149.5]
In [32]:
weather = pd.read_csv('rotterdam_weather_2000_2010.txt', skiprows=11,
                parse_dates=['YYYYMMDD'], index_col='YYYYMMDD', skipinitialspace=True)
weather.TG = 0.1 * weather.TG
weather.RH = 0.1 * weather.RH
weather.EV24 = 0.1 * weather.EV24
weather.loc[weather.RH < 0, 'RH'] = 0
yearly_rain = weather.RH.resample('A', kind='period').sum()
yearly_evap = weather.EV24.resample('A', kind='period').sum()
yearly_temp = weather.TG.resample('A', kind='period').mean()
ax1 = yearly_rain.plot()
ax1 = yearly_evap.plot()
plt.ylabel('Rain/evap (mm/year)')
ax2 = yearly_temp.plot(secondary_y=True)
plt.xlabel('Year')
plt.ylabel('Mean yearly temperature (deg C)')
plt.legend(ax1.get_lines() + ax2.get_lines(),
           ['rain', 'evap', 'temp'], loc='best');