{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "**Task**:\n", " \n", " analysis of several time series data (AO, NAO)\n", "\n", "**Modules**:\n", " \n", " pandas\n", "\n", "[**Notebook file**](http://nbviewer.ipython.org/urls/github.com/koldunovn/earthpy.org/raw/master/content/earthpy_pandas_basics.ipynb) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here I am going to show just some basic [pandas](http://pandas.pydata.org/) stuff for time series analysis, as I think for the Earth Scientists it's the most interesting topic. If you find this small tutorial useful, I encourage you to watch [this video](http://pyvideo.org/video/1198/time-series-data-analysis-with-pandas), where Wes McKinney give extensive introduction to the time series data analysis with pandas.\n", "\n", "On the official website you can find explanation of what problems pandas solve in general, but I can tell you what problem pandas solve for me. It makes analysis and visualisation of 1D data, especially time series, MUCH faster. Before pandas working with time series in python was a pain for me, now it's fun. Ease of use stimulate in-depth exploration of the data: why wouldn't you make some additional analysis if it's just one line of code? Hope you will also find this great tool helpful and useful. So, let's begin.\n", "\n", "Thanks to *Will Bryant* for the 2017 update." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As an example we are going to use time series of [Arctic Oscillation (AO)](http://en.wikipedia.org/wiki/Arctic_oscillation) and [North Atlantic Oscillation (NAO)](http://en.wikipedia.org/wiki/North_Atlantic_oscillation) data sets." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Module import" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we have to import necessary modules:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from pandas import Series, DataFrame, Panel\n", "pd.set_option('display.max_rows',15) # this limit maximum numbers of rows" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And \"switch on\" inline graphic for the notebook:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pandas developing very fast, and while we are going to use only basic functionality, some details may still change in the newer versions. This example will only work in version > 0.8. Check yours:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'0.20.3'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, when we are done with preparations, let's get some data. If you work on Windows download monthly AO data [from here](http://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/monthly.ao.index.b50.current.ascii). If you on *nix machine, you can do it directly from ipython notebook using system call to wget command or on Mac a curl command:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2017-11-07 18:56:56-- http://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/monthly.ao.index.b50.current.ascii\n", "Resolving www.cpc.ncep.noaa.gov... 140.90.101.63\n", "Connecting to www.cpc.ncep.noaa.gov|140.90.101.63|:80... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 20350 (20K) [text/plain]\n", "Saving to: ‘monthly.ao.index.b50.current.ascii’\n", "\n", "monthly.ao.index.b5 100%[===================>] 19.87K 95.0KB/s in 0.2s \n", "\n", "2017-11-07 18:56:57 (95.0 KB/s) - ‘monthly.ao.index.b50.current.ascii’ saved [20350/20350]\n", "\n" ] } ], "source": [ "#!curl http://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/monthly.ao.index.b50.current.ascii >> 'monthly.ao.index.b50.current.ascii'\n", "!wget http://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/monthly.ao.index.b50.current.ascii\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pandas has very good IO capabilities, but we not going to use them in this tutorial in order to keep things simple. For now we open the file simply with numpy loadtxt:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "ao = np.loadtxt('monthly.ao.index.b50.current.ascii')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Every line in the file consist of three elements: year, month, value:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1.95000000e+03, 1.00000000e+00, -6.03100000e-02],\n", " [ 1.95000000e+03, 2.00000000e+00, 6.26810000e-01]])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ao[0:2]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And here is the shape of our array:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(814, 3)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ao.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Time Series" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We would like to convert this data in to time series, that can be manipulated naturally and easily. First step, that we have to do is to create the range of dates for our time series. From the file it is clear, that record starts at January 1950. We will start our dates at January 1950 and generate as many time stamps as we have records. Frequency of the data is one month (freq='M'). " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "dates = pd.date_range('1950-01', periods=ao.shape[0], freq='M')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you see syntax is quite simple, and this is one of the reasons why I love Pandas so much :) You can check if the range of dates is properly generated:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DatetimeIndex(['1950-01-31', '1950-02-28', '1950-03-31', '1950-04-30',\n", " '1950-05-31', '1950-06-30', '1950-07-31', '1950-08-31',\n", " '1950-09-30', '1950-10-31',\n", " ...\n", " '2017-01-31', '2017-02-28', '2017-03-31', '2017-04-30',\n", " '2017-05-31', '2017-06-30', '2017-07-31', '2017-08-31',\n", " '2017-09-30', '2017-10-31'],\n", " dtype='datetime64[ns]', length=814, freq='M')" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dates" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(814,)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dates.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we are ready to create our first time series. Dates from the *dates* variable will be our index, and AO values will be our, hm... values:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "AO = Series(ao[:,2], index=dates)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1950-01-31 -0.060310\n", "1950-02-28 0.626810\n", "1950-03-31 -0.008127\n", "1950-04-30 0.555100\n", "1950-05-31 0.071577\n", "1950-06-30 0.538570\n", "1950-07-31 -0.802480\n", " ... \n", "2017-04-30 -0.088658\n", "2017-05-31 -0.730140\n", "2017-06-30 0.401660\n", "2017-07-31 0.634230\n", "2017-08-31 0.150040\n", "2017-09-30 -0.492450\n", "2017-10-31 0.690340\n", "Freq: M, Length: 814, dtype: float64" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "AO" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can plot complete time series:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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q8usCwd6cQ1dPKfSw8p+jmiSvui9920URNdHYl6/t0Fqpgag3ReS4/1d8fabh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\ndMZB8KiZ3rRhZdXA+/P4HzKgd3PahAFKvfqY6qIxFPbapxS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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "AO.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or its part:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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PK6joBn/P/CyyZrCgWyibydPRuGkdXm356pPJCEbicrOHAAAuZuwJVE03oFaN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\nzb7f7LjdPnY3r7nbxxbvd2+PLd7v9n93J73f+yilU01+x4R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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "AO['1980':'1990'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or even smaller part:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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57XV2h+M3xhjKKuu5PC9DCyuOwaUz0kmJj2ZtiA5bHWtiuA540vv4SeAjY9zf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\n0s8Q1IkhOV7nL6jIdOfSGUQ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "AO['1980-05':'1981-03'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reference to the time periods is done in a very natural way. You, of course, can also get individual values. By number: " ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-2.4842" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "AO[120]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or by index (date in our case):" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1960-01-31 -2.4842\n", "Freq: M, dtype: float64" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "AO['1960-01']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And what if we choose only one year?" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1960-01-31 -2.484200\n", "1960-02-29 -2.212400\n", "1960-03-31 -1.624600\n", "1960-04-30 -0.297310\n", "1960-05-31 -0.857430\n", "1960-06-30 0.054978\n", "1960-07-31 -0.619060\n", "1960-08-31 -1.007900\n", "1960-09-30 -0.381640\n", "1960-10-31 -1.187000\n", "1960-11-30 -0.553230\n", "1960-12-31 -0.342950\n", "Freq: M, dtype: float64" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "AO['1960']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Isn't that great? :)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One bonus example :)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1950-02-28 0.626810\n", "1950-04-30 0.555100\n", "1950-05-31 0.071577\n", "1950-06-30 0.538570\n", "1950-09-30 0.357970\n", "1951-07-31 0.090023\n", "1951-12-31 1.987200\n", " ... \n", "2017-01-31 0.941970\n", "2017-02-28 0.339860\n", "2017-03-31 1.365400\n", "2017-06-30 0.401660\n", "2017-07-31 0.634230\n", "2017-08-31 0.150040\n", "2017-10-31 0.690340\n", "Length: 381, dtype: float64" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "AO[AO > 0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Frame" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's make life a bit more interesting and download more data. This will be NOA time series (Windows users can get it [here](http://www.cpc.ncep.noaa.gov/products/precip/CWlink/pna/norm.nao.monthly.b5001.current.ascii), Mac users can use curl)." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2017-11-07 19:03:09-- http://www.cpc.ncep.noaa.gov/products/precip/CWlink/pna/norm.nao.monthly.b5001.current.ascii\n", "Resolving www.cpc.ncep.noaa.gov... 140.90.101.63\n", "Connecting to www.cpc.ncep.noaa.gov|140.90.101.63|:80... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 19536 (19K) [text/plain]\n", "Saving to: ‘norm.nao.monthly.b5001.current.ascii’\n", "\n", "norm.nao.monthly.b5 100%[===================>] 19.08K 22.6KB/s in 0.8s \n", "\n", "2017-11-07 19:03:10 (22.6 KB/s) - ‘norm.nao.monthly.b5001.current.ascii’ saved [19536/19536]\n", "\n" ] } ], "source": [ "#!curl http://www.cpc.ncep.noaa.gov/products/precip/CWlink/pna/norm.nao.monthly.b5001.current.ascii >> 'norm.nao.monthly.b5001.current.ascii'\n", "!wget http://www.cpc.ncep.noaa.gov/products/precip/CWlink/pna/norm.nao.monthly.b5001.current.ascii" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create Series the same way as we did for AO:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "nao = np.loadtxt('norm.nao.monthly.b5001.current.ascii')\n", "dates_nao = pd.date_range('1950-01', periods=nao.shape[0], freq='M')\n", "NAO = Series(nao[:,2], index=dates_nao)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Time period is the same:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DatetimeIndex(['1950-01-31', '1950-02-28', '1950-03-31', '1950-04-30',\n", " '1950-05-31', '1950-06-30', '1950-07-31', '1950-08-31',\n", " '1950-09-30', '1950-10-31',\n", " ...\n", " '2017-01-31', '2017-02-28', '2017-03-31', '2017-04-30',\n", " '2017-05-31', '2017-06-30', '2017-07-31', '2017-08-31',\n", " '2017-09-30', '2017-10-31'],\n", " dtype='datetime64[ns]', length=814, freq='M')" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "NAO.index" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we create Data Frame, that will contain both AO and NAO data. It sort of an Excel table where the first row contain headers for the columns and firs column is an index:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "aonao = DataFrame({'AO' : AO, 'NAO' : NAO})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One can plot the data straight away:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([,\n", " ], dtype=object)" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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fX5yhO0g1Su0SjE8NoDLhJOoJRd8qJASqwhbu6HSYJjSKA6VVrqYPMQOXEfKERh0VD79b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\nAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "aonao.plot(subplots=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or have a look at the first several rows:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
AONAO
1950-01-31-0.0603100.92
1950-02-280.6268100.40
1950-03-31-0.008127-0.36
1950-04-300.5551000.73
1950-05-310.071577-0.59
\n", "
" ], "text/plain": [ " AO NAO\n", "1950-01-31 -0.060310 0.92\n", "1950-02-28 0.626810 0.40\n", "1950-03-31 -0.008127 -0.36\n", "1950-04-30 0.555100 0.73\n", "1950-05-31 0.071577 -0.59" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aonao.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can reference each column by its name:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1950-01-31 0.920000\n", "1950-02-28 0.400000\n", "1950-03-31 -0.360000\n", "1950-04-30 0.730000\n", "1950-05-31 -0.590000\n", "1950-06-30 -0.060000\n", "1950-07-31 -1.260000\n", " ... \n", "2017-04-30 1.732500\n", "2017-05-31 -1.911000\n", "2017-06-30 0.045036\n", "2017-07-31 1.255600\n", "2017-08-31 -1.097700\n", "2017-09-30 -0.612730\n", "2017-10-31 0.185940\n", "Freq: M, Name: NAO, Length: 814, dtype: float64" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aonao['NAO']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or as method of the Data Frame variable (if name of the variable is a valid python name):" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1950-01-31 0.920000\n", "1950-02-28 0.400000\n", "1950-03-31 -0.360000\n", "1950-04-30 0.730000\n", "1950-05-31 -0.590000\n", "1950-06-30 -0.060000\n", "1950-07-31 -1.260000\n", " ... \n", "2017-04-30 1.732500\n", "2017-05-31 -1.911000\n", "2017-06-30 0.045036\n", "2017-07-31 1.255600\n", "2017-08-31 -1.097700\n", "2017-09-30 -0.612730\n", "2017-10-31 0.185940\n", "Freq: M, Name: NAO, Length: 814, dtype: float64" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aonao.NAO" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can simply add column to the Data Frame:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
AONAODiff
1950-01-31-0.0603100.92-0.980310
1950-02-280.6268100.400.226810
1950-03-31-0.008127-0.360.351872
1950-04-300.5551000.73-0.174900
1950-05-310.071577-0.590.661577
\n", "
" ], "text/plain": [ " AO NAO Diff\n", "1950-01-31 -0.060310 0.92 -0.980310\n", "1950-02-28 0.626810 0.40 0.226810\n", "1950-03-31 -0.008127 -0.36 0.351872\n", "1950-04-30 0.555100 0.73 -0.174900\n", "1950-05-31 0.071577 -0.59 0.661577" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aonao['Diff'] = aonao['AO'] - aonao['NAO']\n", "aonao.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And delete it:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
AONAO
2017-06-300.401660.045036
2017-07-310.634231.255600
2017-08-310.15004-1.097700
2017-09-30-0.49245-0.612730
2017-10-310.690340.185940
\n", "
" ], "text/plain": [ " AO NAO\n", "2017-06-30 0.40166 0.045036\n", "2017-07-31 0.63423 1.255600\n", "2017-08-31 0.15004 -1.097700\n", "2017-09-30 -0.49245 -0.612730\n", "2017-10-31 0.69034 0.185940" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "del aonao['Diff']\n", "aonao.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Slicing will also work:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AONAO
1981-01-31-0.116340.37
1981-02-28-0.331580.92
1981-03-31-1.64470-1.19
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" ], "text/plain": [ " AO NAO\n", "1981-01-31 -0.11634 0.37\n", "1981-02-28 -0.33158 0.92\n", "1981-03-31 -1.64470 -1.19" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aonao['1981-01':'1981-03']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "even in some crazy combinations:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import datetime\n", "aonao.loc[(aonao.AO > 0) & (aonao.NAO < 0) \n", " & (aonao.index > datetime.datetime(1980,1,1)) \n", " & (aonao.index < datetime.datetime(1989,1,1)),\n", " 'NAO'].plot(kind='barh')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we use special [advanced indexing attribute .ix](http://pandas.pydata.org/pandas-docs/stable/indexing.html#advanced-indexing-with-labels). We choose all NAO values in the 1980s for months where AO is positive and NAO is negative, and then plot them. Magic :)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Statistics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Back to simple stuff. We can obtain statistical information over elements of the Data Frame. Default is column wise:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AO -0.111329\n", "NAO -0.016255\n", "dtype: float64" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aonao.mean()" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AO 3.4953\n", "NAO 3.0400\n", "dtype: float64" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aonao.max()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AO -4.2657\n", "NAO -3.1800\n", "dtype: float64" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aonao.min()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also do it row-wise:" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1950-01-31 0.429845\n", "1950-02-28 0.513405\n", "1950-03-31 -0.184064\n", "1950-04-30 0.642550\n", "1950-05-31 -0.259211\n", "1950-06-30 0.239285\n", "1950-07-31 -1.031240\n", " ... \n", "2017-04-30 0.821921\n", "2017-05-31 -1.320570\n", "2017-06-30 0.223348\n", "2017-07-31 0.944915\n", "2017-08-31 -0.473830\n", "2017-09-30 -0.552590\n", "2017-10-31 0.438140\n", "Freq: M, Length: 814, dtype: float64" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aonao.mean(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or get everything at once:" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AONAO
count814.000000814.000000
mean-0.111329-0.016255
std1.0049801.012511
min-4.265700-3.180000
25%-0.651515-0.757500
50%-0.0399310.042518
75%0.4783150.689250
max3.4953003.040000
\n", "
" ], "text/plain": [ " AO NAO\n", "count 814.000000 814.000000\n", "mean -0.111329 -0.016255\n", "std 1.004980 1.012511\n", "min -4.265700 -3.180000\n", "25% -0.651515 -0.757500\n", "50% -0.039931 0.042518\n", "75% 0.478315 0.689250\n", "max 3.495300 3.040000" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aonao.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Resampling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pandas provide easy way to resample data to different time frequency. Two main parameters for resampling is time period you resemple to and the method that you use. By default the method is mean. Following example calculates annual ('A') mean:" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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hxytf+u+3yb/h6we/5jznDA91eAiAfQ3GzQnzC8Nfj/+FFWNWgKIodAjrgOty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\nFKUpZufN5tiZc1lZnWjD9hTqGTVe2fIKvX/Vm9zncnlo5UM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dWK4RRGQwMMVuPwuctY83\niMg+rFn0QaCP1yX6AIcjKbM/AumBJe9aY8xx+73lWH7dV3CXHg4zODfrB/fdjwLgHWNMDXBURN4H\n8rFmZ96/cmJaD2NMLfBjp5+IfADsAcqI8v0QkXgsg/knY8wSu7lERLKNMcW2W+eo3X4Q/3/3g8Dl\njdrXhFPuxoSoRyAC6Rd1oj7zF5Ge9nMH4OfAc/brHiISZx8PBAZhLTIWAxUiMs6OYvgesDQqwnsR\nSA9gBTBKRDrbfuaJwA4X6uG0TcfyWwKen75u0uNL4Ao7yiQZa5FxF9bC6iARGSAiCVhfcm9GXvKG\nNPH/0dmWHxGZBNQaY6L+ubLHXAjsNMb8yuutNwEnYmeWl0xvAt+z78c4oNzWYQUwWUS62RE1k+22\niNACPQIRk58rIOLRPoux3Dk1WN+I/wHch7WSvht4gnMbz27CyupZBGwErvO6Tj6WL3Qf8DvnnFjU\nw+4/09ZlG/CUi/W4HPjIz3VcowfQBXjNvh87gP/2us61dv99wMOR1KEFevQHPsNaiFyJlckx6vcD\ny7VpsCLcNtuPa7Gi3FZh/TpZBaTb/QWYb8u6Fcj3utYdwF77MTvC9yJUPbLse3YSa/H9INbCe9Q/\nV4EeusNXURSlHRJ1t4+iKIoSedT4K4qitEPU+CuKorRD1PgriqK0Q9T4K4qitEPU+CuKorRD1Pgr\niqK0Q9T4K4qitEP+H7xyyGhkqsblAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "AO_mm = AO.resample(\"A\").mean()\n", "AO_mm.plot(style='g--')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "median:" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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NULv/2heeAR4YNj2iVNNLNf3YyXA2W8KWZFhvNUzF1JNO1kJnYKWkoWj9Kvmq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\nwXxXS9olaY+kFVXlMyRtkLRb0q8ljWmlPmZm1rpWzxh2AF8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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "AO_mm = AO.resample(\"A\").median()\n", "AO_mm.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can use your methods for resampling, for example np.max (in this case we change resampling frequency to 3 years):" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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C8L5//JZ16eWZ4ia4t/WN4faaqc0j+7aVc/LyQMgvs+YytZhauvy/0MFFnmjl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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "AO_mm = AO.resample(\"3A\").apply(np.max)\n", "AO_mm.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can specify several functions at once as a list:" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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OlVNaQ2xyPg/MC+KVK8Z1MPTtkSSJW2f4I0mwNs7w+7Cw6hz1Tc14u4iHw3BHG0K9HNiR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\n2Ttm42HvwYsxLxLXKY6Z0TPZen5rrYyfzWc3c8+6e/jvyf/y8dGP+T7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H4qP0sbjfU+FJlE/UsHvsZ5tMX5pxwX29Sz8PP56Y/gQn60+yKX+TWfFizWOX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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "AO_mm = AO.resample(\"A\").apply(['mean', np.min, np.max])\n", "AO_mm['1900':'2020'].plot(subplots=True)\n", "AO_mm['1900':'2020'].plot()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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meanaminamax
1950-12-31-0.199501-1.928100.62681
1951-12-31-0.364626-1.934101.98720
1952-12-31-0.674990-1.890900.53852
1953-12-31-0.016515-1.255801.06830
1954-12-31-0.000770-1.656100.55259
1955-12-31-0.361816-1.568100.76012
1956-12-31-0.162823-2.028601.39110
............
2011-12-310.525719-1.683102.27480
2012-12-31-0.181383-1.748601.03710
2013-12-310.000956-3.185402.02900
2014-12-31-0.066734-1.134401.20580
2015-12-310.629559-1.107901.94500
2016-12-31-0.114137-1.917301.78640
2017-12-310.321225-0.730141.36540
\n", "

68 rows × 3 columns

\n", "
" ], "text/plain": [ " mean amin amax\n", "1950-12-31 -0.199501 -1.92810 0.62681\n", "1951-12-31 -0.364626 -1.93410 1.98720\n", "1952-12-31 -0.674990 -1.89090 0.53852\n", "1953-12-31 -0.016515 -1.25580 1.06830\n", "1954-12-31 -0.000770 -1.65610 0.55259\n", "1955-12-31 -0.361816 -1.56810 0.76012\n", "1956-12-31 -0.162823 -2.02860 1.39110\n", "... ... ... ...\n", "2011-12-31 0.525719 -1.68310 2.27480\n", "2012-12-31 -0.181383 -1.74860 1.03710\n", "2013-12-31 0.000956 -3.18540 2.02900\n", "2014-12-31 -0.066734 -1.13440 1.20580\n", "2015-12-31 0.629559 -1.10790 1.94500\n", "2016-12-31 -0.114137 -1.91730 1.78640\n", "2017-12-31 0.321225 -0.73014 1.36540\n", "\n", "[68 rows x 3 columns]" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "AO_mm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Moving (rolling) statistics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pandas provide good collection of moving statistics." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Rolling mean:" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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YXDD+AgDAiz9/sei2yIQlPge3ZOYUUv/o8Y+i8ZpGvHrGqwAKm31IWbamWi2V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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "aonao.rolling(window=12, center=False).mean().plot(style='-g')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Rolling correlation:" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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B3wDTtNYHATf423sDs4AjgSOAWUqp9K5AEEfS1dLPJPfOlqot1nZSLH3bRO7v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\n9Nv7zBvcDVzwL59BYCzr44Y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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "aonao.AO.rolling(window=120).corr(other=aonao.NAO).plot(style='-g')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By the way getting correlation coefficients for members of the Data Frame is as simple as:" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " AO NAO\n", "AO 1.000000 0.604209\n", "NAO 0.604209 1.000000" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aonao.corr()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's it. I hope you at least get a rough impression of what pandas can do for you. Comments are very welcome (below). If you have intresting examples of pandas usage in Earth Science, we would be happy to put them on [EarthPy](http://earthpy.org)." ] } ], "metadata": { "Author": "Nikolay Koldunov", "Category": "Data processing", "Date": "2013-3-30", "Tags": "pandas, data analysis, visualization", "Title": "Time series analysis with pandas", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" }, "slug": "pandas-basics" }, "nbformat": 4, "nbformat_minor": 1 }