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# We will probably need these
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import os
Loading real data with Pandas
Real data is often stored in CSV files (Comma Separated Values).
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. We will use only a few functions of the pandas package here. Full information on pandas can be found on the pandas website.
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 (Notepad, Textedit, even Word if you have to). 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
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rain = pd.read_csv('annual_precip.csv', skiprows=2, index_col=0)
# Examine the read file
print('First five lines of rain dataset:')
print(rain.head(10))
Get data for specific countries:
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print('\nAverage annual rainfall in Panama is',rain.loc['Panama','precip'],'mm/year')
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print('\nLand area of the Netherlands is', rain.loc['Netherlands','area'], 'thousand km^2/year')
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print('\nCountries where average rainfall is below 200 mm/year')
print(rain[ rain.precip < 200 ])
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print('\nCountries where average rainfall is above 2500 mm/year')
print(rain[ rain.precip > 2500 ])
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print('Countries with almost the same rainfall as Netherlands')
print(rain[ abs(rain.loc['Netherlands','precip'] - rain.precip) < 50 ])
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
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data = pd.read_csv('timeseries1.dat', parse_dates=[0])
print(data)
The rows of the DataFrame data are numbered, as we have not told pandas what column to use as the index of the rows. The first column of the DataFrame data has datetime values. We can access, for example, the year, month, or day with the dot syntax
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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)
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
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data = pd.read_csv('timeseries2.dat', parse_dates=[0])
print(data)
print('hour of row 0:', data.iloc[0,0].hour)
print('minute of row 0:', data.iloc[0,0].minute)
print('time of row 0:', data.iloc[0,0].time())
Here we do the same thing, but additionally tell read_csv to use column 0 as index_col
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data = pd.read_csv('timeseries2.dat', parse_dates=[0], index_col=0)
print(data)
data.plot(kind='bar')
plt.show()
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:
- 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
plotfunction ofpandasto create a line plot of the daily rainfall with the number of the day (so not the date) along the horizontal axis. - Use
matplotlibfunctions 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.
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rain = pd.read_csv('rotterdam_rainfall_2012.txt',skiprows=9,
parse_dates=['YYYYMMDD'],skipinitialspace=True, index_col='YYYYMMDD')
rain.head(10)
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These data need some manipulation. The rain values are given in 0.1 mm/d where -1 means 0.0005 mm/d.
To convert the RH to mm/d, remove the station number and change the name of the index
We can drop the column 'STN' and the result is a DataFrame
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rain.drop('STN', axis=1)
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We could also just select column 'RH' and then we obtain a time series.
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rain = rain['RH']
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rain
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rain[rain < 0] = 0.5 rain
This implies automatic conversion from int to float dtype.
Then divide by 10 to obtain mm/d.
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rain = rain/10.
rain
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rain.plot()
plt.xlabel('day')
plt.ylabel('daily rainfall (mm/day)')
plt.show()
print('Maximum daily rainfall',rain.max())
print('Date of maximum daily rainfall',rain.argmax())
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.
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rain.resample('M').sum()
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monthlyRain = rain.resample('M').sum()
monthlyRain.plot(kind='bar')
plt.xticks(np.arange(12),['J','F','M','A','M','J','J','A','S','O','N','D'])
plt.xlabel('month, 2012')
plt.ylabel('monthly rainfall (mm/month)')
plt.show()
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data = read_csv('timeseries1.dat', parse_dates=[0], index_col=0)
print(data)
print('data on April 1:',data.loc['2014-04-01'])
print('data on April 2:',data.loc['2014-04-02'])
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 computed 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 how is used to tell pandas how to compute the resampled data. This can be many things, like 'mean' for the mean (that is the default), 'sum' for the total, 'min', 'max', etc. The keyword argument kind is used to tell pandas where to assign the computed value 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. Calculating the montly totals and making a bar graph can now be done with pandas as follows.
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rain = pd.read_csv('rotterdam_rainfall_2012.txt', skiprows=9,
parse_dates=['YYYYMMDD'], index_col='YYYYMMDD',
skipinitialspace=True)
rain.RH[rain.RH<0] = 0.5
rain.RH = rain.RH * 0.1 # Convert to mm/day
monthlyrain = rain.RH.resample('M', kind='period').sum()
print(monthlyrain)
monthlyrain.plot(kind='bar')
plt.ylabel('mm/month')
plt.xlabel('month')
plt.show()
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 temperature using the
plotfunction ofpandas. Make sure to plot the mean temperature on the secondary $y$-axis (use the help function to find out how).
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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.RH[weather.RH<0] = 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()
yearly_rain.plot()
yearly_evap.plot()
yearly_temp.plot(secondary_y=True)
plt.xlabel('Year')
plt.ylabel('Rain/evap (mm/year)')
plt.show()
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