{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Wind Statistics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Introduction:\n", "\n", "The data have been modified to contain some missing values, identified by NaN. \n", "Using pandas should make this exercise\n", "easier, in particular for the bonus question.\n", "\n", "You should be able to perform all of these operations without using\n", "a for loop or other looping construct.\n", "\n", "\n", "1. The data in 'wind.data' has the following format:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'\\nYr Mo Dy RPT VAL ROS KIL SHA BIR DUB CLA MUL CLO BEL MAL\\n61 1 1 15.04 14.96 13.17 9.29 NaN 9.87 13.67 10.25 10.83 12.58 18.50 15.04\\n61 1 2 14.71 NaN 10.83 6.50 12.62 7.67 11.50 10.04 9.79 9.67 17.54 13.83\\n61 1 3 18.50 16.88 12.33 10.13 11.17 6.17 11.25 NaN 8.50 7.67 12.75 12.71\\n'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"\"\"\n", "Yr Mo Dy RPT VAL ROS KIL SHA BIR DUB CLA MUL CLO BEL MAL\n", "61 1 1 15.04 14.96 13.17 9.29 NaN 9.87 13.67 10.25 10.83 12.58 18.50 15.04\n", "61 1 2 14.71 NaN 10.83 6.50 12.62 7.67 11.50 10.04 9.79 9.67 17.54 13.83\n", "61 1 3 18.50 16.88 12.33 10.13 11.17 6.17 11.25 NaN 8.50 7.67 12.75 12.71\n", "\"\"\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " The first three columns are year, month and day. The\n", " remaining 12 columns are average windspeeds in knots at 12\n", " locations in Ireland on that day. \n", "\n", " More information about the dataset go [here](wind.desc)." ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "### Step 1. Import the necessary libraries" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import datetime" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 2. Import the dataset from this [address](https://github.com/guipsamora/pandas_exercises/blob/master/Stats/Wind_Stats/wind.data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 3. Assign it to a variable called data and replace the first 3 columns by a proper datetime index." ] }, { "cell_type": "code", "execution_count": 414, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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Yr_Mo_DyRPTVALROSKILSHABIRDUBCLAMULCLOBELMAL
02061-01-0115.0414.9613.179.29NaN9.8713.6710.2510.8312.5818.5015.04
12061-01-0214.71NaN10.836.5012.627.6711.5010.049.799.6717.5413.83
22061-01-0318.5016.8812.3310.1311.176.1711.25NaN8.507.6712.7512.71
32061-01-0410.586.6311.754.584.542.888.631.795.835.885.4610.88
42061-01-0513.3313.2511.426.1710.718.2111.926.5410.9210.3412.9211.83
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" ], "text/plain": [ " Yr_Mo_Dy RPT VAL ROS KIL SHA BIR DUB CLA MUL \\\n", "0 2061-01-01 15.04 14.96 13.17 9.29 NaN 9.87 13.67 10.25 10.83 \n", "1 2061-01-02 14.71 NaN 10.83 6.50 12.62 7.67 11.50 10.04 9.79 \n", "2 2061-01-03 18.50 16.88 12.33 10.13 11.17 6.17 11.25 NaN 8.50 \n", "3 2061-01-04 10.58 6.63 11.75 4.58 4.54 2.88 8.63 1.79 5.83 \n", "4 2061-01-05 13.33 13.25 11.42 6.17 10.71 8.21 11.92 6.54 10.92 \n", "\n", " CLO BEL MAL \n", "0 12.58 18.50 15.04 \n", "1 9.67 17.54 13.83 \n", "2 7.67 12.75 12.71 \n", "3 5.88 5.46 10.88 \n", "4 10.34 12.92 11.83 " ] }, "execution_count": 414, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# parse_dates gets 0, 1, 2 columns and parses them as the index\n", "data = pd.read_table(\"wind.data\", sep = \"\\s+\", parse_dates = [[0,1,2]]) \n", "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 4. Year 2061? Do we really have data from this year? Create a function to fix it and apply it." ] }, { "cell_type": "code", "execution_count": 415, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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Yr_Mo_DyRPTVALROSKILSHABIRDUBCLAMULCLOBELMAL
01961-01-0115.0414.9613.179.29NaN9.8713.6710.2510.8312.5818.5015.04
11961-01-0214.71NaN10.836.5012.627.6711.5010.049.799.6717.5413.83
21961-01-0318.5016.8812.3310.1311.176.1711.25NaN8.507.6712.7512.71
31961-01-0410.586.6311.754.584.542.888.631.795.835.885.4610.88
41961-01-0513.3313.2511.426.1710.718.2111.926.5410.9210.3412.9211.83
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" ], "text/plain": [ " Yr_Mo_Dy RPT VAL ROS KIL SHA BIR DUB CLA MUL \\\n", "0 1961-01-01 15.04 14.96 13.17 9.29 NaN 9.87 13.67 10.25 10.83 \n", "1 1961-01-02 14.71 NaN 10.83 6.50 12.62 7.67 11.50 10.04 9.79 \n", "2 1961-01-03 18.50 16.88 12.33 10.13 11.17 6.17 11.25 NaN 8.50 \n", "3 1961-01-04 10.58 6.63 11.75 4.58 4.54 2.88 8.63 1.79 5.83 \n", "4 1961-01-05 13.33 13.25 11.42 6.17 10.71 8.21 11.92 6.54 10.92 \n", "\n", " CLO BEL MAL \n", "0 12.58 18.50 15.04 \n", "1 9.67 17.54 13.83 \n", "2 7.67 12.75 12.71 \n", "3 5.88 5.46 10.88 \n", "4 10.34 12.92 11.83 " ] }, "execution_count": 415, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The problem is that the dates are 2061 and so on...\n", "\n", "# function that uses datetime\n", "def fix_century(x):\n", " year = x.year - 100 if x.year > 1989 else x.year\n", " return datetime.date(year, x.month, x.day)\n", "\n", "# apply the function fix_century on the column and replace the values to the right ones\n", "data['Yr_Mo_Dy'] = data['Yr_Mo_Dy'].apply(fix_century)\n", "\n", "# data.info()\n", "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 5. Set the right dates as the index. Pay attention at the data type, it should be datetime64[ns]." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "ename": "NameError", "evalue": "name 'pd' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# transform Yr_Mo_Dy it to date type datetime64\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Yr_Mo_Dy\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Yr_Mo_Dy\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# set 'Yr_Mo_Dy' as the index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Yr_Mo_Dy'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined" ] } ], "source": [ "# transform Yr_Mo_Dy it to date type datetime64\n", "data[\"Yr_Mo_Dy\"] = pd.to_datetime(data[\"Yr_Mo_Dy\"])\n", "\n", "# set 'Yr_Mo_Dy' as the index\n", "data = data.set_index('Yr_Mo_Dy')\n", "\n", "data.head()\n", "# data.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 6. Compute how many values are missing for each location over the entire record. \n", "#### They should be ignored in all calculations below. " ] }, { "cell_type": "code", "execution_count": 423, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "RPT 6\n", "VAL 3\n", "ROS 2\n", "KIL 5\n", "SHA 2\n", "BIR 0\n", "DUB 3\n", "CLA 2\n", "MUL 3\n", "CLO 1\n", "BEL 0\n", "MAL 4\n", "dtype: int64" ] }, "execution_count": 423, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# \"Number of non-missing values for each location: \"\n", "data.isnull().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 7. Compute how many non-missing values there are in total." ] }, { "cell_type": "code", "execution_count": 424, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "RPT 6\n", "VAL 9\n", "ROS 10\n", "KIL 7\n", "SHA 10\n", "BIR 12\n", "DUB 9\n", "CLA 10\n", "MUL 9\n", "CLO 11\n", "BEL 12\n", "MAL 8\n", "dtype: int64" ] }, "execution_count": 424, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# number of columns minus the number of missing values for each location\n", "data.shape[1] - data.isnull().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 8. Calculate the mean windspeeds of the windspeeds over all the locations and all the times.\n", "#### A single number for the entire dataset." ] }, { "cell_type": "code", "execution_count": 426, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "10.227982360836924" ] }, "execution_count": 426, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# print 'Mean over all values is: '\n", "data.mean().mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 9. Create a DataFrame called loc_stats and calculate the min, max and mean windspeeds and standard deviations of the windspeeds at each location over all the days \n", "\n", "#### A different set of numbers for each location." ] }, { "cell_type": "code", "execution_count": 264, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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minmaxmeanstd
RPT0.6735.8012.3629875.618413
VAL0.2133.3710.6443145.267356
ROS1.5033.8411.6605265.008450
KIL0.0028.466.3064683.605811
SHA0.1337.5410.4558344.936125
BIR0.0026.167.0922543.968683
DUB0.0030.379.7973434.977555
CLA0.0031.088.4950534.499449
MUL0.0025.888.4935904.166872
CLO0.0428.218.7073324.503954
BEL0.1342.3813.1210075.835037
MAL0.6742.5415.5990796.699794
\n", "
" ], "text/plain": [ " min max mean std\n", "RPT 0.67 35.80 12.362987 5.618413\n", "VAL 0.21 33.37 10.644314 5.267356\n", "ROS 1.50 33.84 11.660526 5.008450\n", "KIL 0.00 28.46 6.306468 3.605811\n", "SHA 0.13 37.54 10.455834 4.936125\n", "BIR 0.00 26.16 7.092254 3.968683\n", "DUB 0.00 30.37 9.797343 4.977555\n", "CLA 0.00 31.08 8.495053 4.499449\n", "MUL 0.00 25.88 8.493590 4.166872\n", "CLO 0.04 28.21 8.707332 4.503954\n", "BEL 0.13 42.38 13.121007 5.835037\n", "MAL 0.67 42.54 15.599079 6.699794" ] }, "execution_count": 264, "metadata": {}, "output_type": "execute_result" } ], "source": [ "loc_stats = pd.DataFrame()\n", "\n", "loc_stats['min'] = data.min() # min\n", "loc_stats['max'] = data.max() # max \n", "loc_stats['mean'] = data.mean() # mean\n", "loc_stats['std'] = data.std() # standard deviations\n", "\n", "loc_stats" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 10. Create a DataFrame called day_stats and calculate the min, max and mean windspeed and standard deviations of the windspeeds across all the locations at each day.\n", "\n", "#### A different set of numbers for each day." ] }, { "cell_type": "code", "execution_count": 404, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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minmaxmeanstd
01.018.5012.0166674.382798
11.017.5410.4750004.260110
21.018.5010.7550004.664914
31.011.756.1869233.435771
41.013.339.8892313.551768
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" ], "text/plain": [ " min max mean std\n", "0 1.0 18.50 12.016667 4.382798\n", "1 1.0 17.54 10.475000 4.260110\n", "2 1.0 18.50 10.755000 4.664914\n", "3 1.0 11.75 6.186923 3.435771\n", "4 1.0 13.33 9.889231 3.551768" ] }, "execution_count": 404, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# create the dataframe\n", "day_stats = pd.DataFrame()\n", "\n", "# this time we determine axis equals to one so it gets each row.\n", "day_stats['min'] = data.min(axis = 1) # min\n", "day_stats['max'] = data.max(axis = 1) # max \n", "day_stats['mean'] = data.mean(axis = 1) # mean\n", "day_stats['std'] = data.std(axis = 1) # standard deviations\n", "\n", "day_stats.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 11. Find the average windspeed in January for each location. \n", "#### Treat January 1961 and January 1962 both as January." ] }, { "cell_type": "code", "execution_count": 427, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "RPT 14.847325\n", "VAL 12.914560\n", "ROS 13.299624\n", "KIL 7.199498\n", "SHA 11.667734\n", "BIR 8.054839\n", "DUB 11.819355\n", "CLA 9.512047\n", "MUL 9.543208\n", "CLO 10.053566\n", "BEL 14.550520\n", "MAL 18.028763\n", "dtype: float64" ] }, "execution_count": 427, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# print \"January windspeeds:\"\n", "\n", "# creates a new column 'date' and gets the values from the index\n", "data['date'] = data.index\n", "\n", "# creates a column for each value from date\n", "data['month'] = data['date'].apply(lambda date: date.month)\n", "data['year'] = data['date'].apply(lambda date: date.year)\n", "data['day'] = data['date'].apply(lambda date: date.day)\n", "\n", "# gets all value from the month 1 and assign to janyary_winds\n", "january_winds = data.query('month == 1')\n", "\n", "# gets the mean from january_winds, using .loc to not print the mean of month, year and day\n", "january_winds.loc[:,'RPT':\"MAL\"].mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 12. Downsample the record to a yearly frequency for each location." ] }, { "cell_type": "code", "execution_count": 428, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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RPTVALROSKILSHABIRDUBCLAMULCLOBELMALdatemonthyearday
Yr_Mo_Dy
1961-01-0115.0414.9613.179.29NaN9.8713.6710.2510.8312.5818.5015.041961-01-01119611
1962-01-019.293.4211.543.502.211.9610.412.793.545.174.387.921962-01-01119621
1963-01-0115.5913.6219.798.3812.2510.0023.4515.7113.5914.3717.5834.131963-01-01119631
1964-01-0125.8022.1318.2113.2521.2914.7914.1219.5813.2516.7528.9621.001964-01-01119641
1965-01-019.5411.929.004.386.085.2110.256.085.718.6312.0417.411965-01-01119651
1966-01-0122.0421.5017.0812.7522.1715.5921.7918.1216.6617.8328.3323.791966-01-01119661
1967-01-016.464.466.503.216.673.7911.383.837.719.0810.6720.911967-01-01119671
1968-01-0130.0417.8816.2516.2521.7912.5418.1616.6218.7517.6222.2527.291968-01-01119681
1969-01-016.131.635.411.082.541.008.502.424.586.349.1716.711969-01-01119691
1970-01-019.592.9611.793.426.134.089.004.467.293.507.3313.001970-01-01119701
1971-01-013.710.794.710.171.421.044.630.751.541.084.219.541971-01-01119711
1972-01-019.293.6314.544.256.754.4213.005.3310.048.548.7119.171972-01-01119721
1973-01-0116.5015.9214.627.418.2911.2113.547.7910.4610.7913.379.711973-01-01119731
1974-01-0123.2116.5416.089.7515.8311.469.5413.5413.8316.6617.2125.291974-01-01119741
1975-01-0114.0413.5411.295.4612.585.588.128.969.295.177.7111.631975-01-01119751
1976-01-0118.3417.6714.838.0016.6210.1313.179.0413.135.7511.3814.961976-01-01119761
1977-01-0120.0411.9220.259.139.298.0410.755.889.009.0014.8825.701977-01-01119771
1978-01-018.337.127.713.548.507.5014.7110.0011.8310.0015.0920.461978-01-01119781
\n", "
" ], "text/plain": [ " RPT VAL ROS KIL SHA BIR DUB CLA MUL \\\n", "Yr_Mo_Dy \n", "1961-01-01 15.04 14.96 13.17 9.29 NaN 9.87 13.67 10.25 10.83 \n", "1962-01-01 9.29 3.42 11.54 3.50 2.21 1.96 10.41 2.79 3.54 \n", "1963-01-01 15.59 13.62 19.79 8.38 12.25 10.00 23.45 15.71 13.59 \n", "1964-01-01 25.80 22.13 18.21 13.25 21.29 14.79 14.12 19.58 13.25 \n", "1965-01-01 9.54 11.92 9.00 4.38 6.08 5.21 10.25 6.08 5.71 \n", "1966-01-01 22.04 21.50 17.08 12.75 22.17 15.59 21.79 18.12 16.66 \n", "1967-01-01 6.46 4.46 6.50 3.21 6.67 3.79 11.38 3.83 7.71 \n", "1968-01-01 30.04 17.88 16.25 16.25 21.79 12.54 18.16 16.62 18.75 \n", "1969-01-01 6.13 1.63 5.41 1.08 2.54 1.00 8.50 2.42 4.58 \n", "1970-01-01 9.59 2.96 11.79 3.42 6.13 4.08 9.00 4.46 7.29 \n", "1971-01-01 3.71 0.79 4.71 0.17 1.42 1.04 4.63 0.75 1.54 \n", "1972-01-01 9.29 3.63 14.54 4.25 6.75 4.42 13.00 5.33 10.04 \n", "1973-01-01 16.50 15.92 14.62 7.41 8.29 11.21 13.54 7.79 10.46 \n", "1974-01-01 23.21 16.54 16.08 9.75 15.83 11.46 9.54 13.54 13.83 \n", "1975-01-01 14.04 13.54 11.29 5.46 12.58 5.58 8.12 8.96 9.29 \n", "1976-01-01 18.34 17.67 14.83 8.00 16.62 10.13 13.17 9.04 13.13 \n", "1977-01-01 20.04 11.92 20.25 9.13 9.29 8.04 10.75 5.88 9.00 \n", "1978-01-01 8.33 7.12 7.71 3.54 8.50 7.50 14.71 10.00 11.83 \n", "\n", " CLO BEL MAL date month year day \n", "Yr_Mo_Dy \n", "1961-01-01 12.58 18.50 15.04 1961-01-01 1 1961 1 \n", "1962-01-01 5.17 4.38 7.92 1962-01-01 1 1962 1 \n", "1963-01-01 14.37 17.58 34.13 1963-01-01 1 1963 1 \n", "1964-01-01 16.75 28.96 21.00 1964-01-01 1 1964 1 \n", "1965-01-01 8.63 12.04 17.41 1965-01-01 1 1965 1 \n", "1966-01-01 17.83 28.33 23.79 1966-01-01 1 1966 1 \n", "1967-01-01 9.08 10.67 20.91 1967-01-01 1 1967 1 \n", "1968-01-01 17.62 22.25 27.29 1968-01-01 1 1968 1 \n", "1969-01-01 6.34 9.17 16.71 1969-01-01 1 1969 1 \n", "1970-01-01 3.50 7.33 13.00 1970-01-01 1 1970 1 \n", "1971-01-01 1.08 4.21 9.54 1971-01-01 1 1971 1 \n", "1972-01-01 8.54 8.71 19.17 1972-01-01 1 1972 1 \n", "1973-01-01 10.79 13.37 9.71 1973-01-01 1 1973 1 \n", "1974-01-01 16.66 17.21 25.29 1974-01-01 1 1974 1 \n", "1975-01-01 5.17 7.71 11.63 1975-01-01 1 1975 1 \n", "1976-01-01 5.75 11.38 14.96 1976-01-01 1 1976 1 \n", "1977-01-01 9.00 14.88 25.70 1977-01-01 1 1977 1 \n", "1978-01-01 10.00 15.09 20.46 1978-01-01 1 1978 1 " ] }, "execution_count": 428, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.query('month == 1 and day == 1')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 13. Downsample the record to a monthly frequency for each location." ] }, { "cell_type": "code", "execution_count": 429, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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RPTVALROSKILSHABIRDUBCLAMULCLOBELMALdatemonthyearday
Yr_Mo_Dy
1961-01-0115.0414.9613.179.29NaN9.8713.6710.2510.8312.5818.5015.041961-01-01119611
1961-02-0114.2515.129.045.8812.087.1710.173.636.505.509.178.001961-02-01219611
1961-03-0112.6713.1311.796.429.798.5410.2513.29NaN12.2120.62NaN1961-03-01319611
1961-04-018.386.348.336.759.339.5411.678.2111.216.4611.967.171961-04-01419611
1961-05-0115.8713.8815.379.7913.4610.179.9614.049.759.9218.6311.121961-05-01519611
1961-06-0115.929.5912.048.7911.546.049.758.299.3310.3410.6712.121961-06-01619611
1961-07-017.216.837.714.428.464.796.716.005.797.966.968.711961-07-01719611
1961-08-019.595.095.544.638.295.254.215.255.375.418.389.081961-08-01819611
1961-09-015.581.134.963.044.252.254.632.713.676.004.795.411961-09-01919611
1961-10-0114.2512.877.878.0013.007.755.839.007.085.2911.794.041961-10-011019611
1961-11-0113.2113.1314.338.5412.1710.2113.0812.1710.9213.5420.1720.041961-11-011119611
1961-12-019.677.758.003.966.002.757.252.505.585.587.7911.171961-12-011219611
1962-01-019.293.4211.543.502.211.9610.412.793.545.174.387.921962-01-01119621
1962-02-0119.1213.9612.2110.5815.7110.6315.7111.0813.1712.6217.6722.711962-02-01219621
1962-03-018.214.839.004.836.002.217.961.874.083.924.085.411962-03-01319621
1962-04-0114.3312.2511.8710.3714.9211.0019.7911.6714.0915.4616.6223.581962-04-01419621
1962-05-019.629.543.583.338.753.752.252.581.672.377.293.251962-05-01519621
1962-06-015.886.298.675.215.004.255.915.414.799.255.2510.711962-06-01619621
1962-07-018.674.176.926.718.175.6611.179.388.7511.1210.2517.081962-07-01719621
1962-08-014.585.376.042.297.873.714.462.584.004.797.217.461962-08-01819621
1962-09-0110.0012.0810.969.259.297.627.418.757.679.6214.5811.921962-09-01919621
1962-10-0114.587.8319.2110.0811.548.3813.2910.638.2112.9218.0518.121962-10-011019621
1962-11-0116.8813.2516.008.9613.4611.4610.4610.1710.3713.2114.8315.161962-11-011119621
1962-12-0118.3815.4111.756.7912.218.048.4210.835.669.0811.5011.501962-12-011219621
1963-01-0115.5913.6219.798.3812.2510.0023.4515.7113.5914.3717.5834.131963-01-01119631
1963-02-0115.417.6224.6711.429.218.1714.047.547.5410.0810.1717.671963-02-01219631
1963-03-0116.7519.6717.678.8719.0815.3716.2114.2911.299.2119.9219.791963-03-01319631
1963-04-0110.549.5912.467.339.469.5911.7911.879.7910.7113.3718.211963-04-01419631
1963-05-0118.7914.1713.5911.6314.1711.9614.4612.4612.8713.9615.2921.621963-05-01519631
1963-06-0113.376.8712.008.5010.049.4210.9212.9611.7911.0410.9213.671963-06-01619631
...................................................
1976-07-018.501.756.582.132.752.215.372.045.884.504.9610.631976-07-01719761
1976-08-0113.008.388.635.8312.928.2513.009.4210.5811.3414.2120.251976-08-01819761
1976-09-0111.8711.007.386.877.758.3310.346.4610.179.2912.7519.551976-09-01919761
1976-10-0110.966.7110.414.637.585.045.045.546.503.926.795.001976-10-011019761
1976-11-0113.9615.6710.296.4612.799.0810.009.6710.2111.6323.0921.961976-11-011119761
1976-12-0113.4616.429.214.5410.758.6710.884.838.795.918.8313.671976-12-011219761
1977-01-0120.0411.9220.259.139.298.0410.755.889.009.0014.8825.701977-01-01119771
1977-02-0111.839.7111.004.258.588.716.175.668.297.5811.7116.501977-02-01219771
1977-03-018.6314.8310.293.756.638.795.008.127.876.4213.5413.671977-03-01319771
1977-04-0121.6716.0017.3313.5920.8315.9625.6217.6219.4120.6724.3730.091977-04-01419771
1977-05-016.427.128.673.584.584.006.756.133.334.5019.2112.381977-05-01519771
1977-06-017.085.259.712.832.213.505.291.422.000.925.215.631977-06-01619771
1977-07-0115.4116.2917.086.2511.8311.8312.2910.5810.417.2117.377.831977-07-01719771
1977-08-014.332.964.422.330.961.084.961.872.332.0410.509.831977-08-01819771
1977-09-0117.3716.3316.838.5814.4611.8315.0913.9213.2913.8823.2925.171977-09-01919771
1977-10-0116.7515.3412.259.4216.3811.3818.5013.9214.0914.4622.3429.671977-10-011019771
1977-11-0116.7111.5412.174.178.547.1711.126.468.256.2111.0415.631977-11-011119771
1977-12-0113.3710.9212.422.375.796.138.967.386.295.718.5412.421977-12-011219771
1978-01-018.337.127.713.548.507.5014.7110.0011.8310.0015.0920.461978-01-01119781
1978-02-0127.2524.2118.1617.4627.5418.0520.9625.0420.0417.5027.7121.121978-02-01219781
1978-03-0115.046.2116.047.876.426.6712.298.0010.589.335.4117.001978-03-01319781
1978-04-013.427.582.711.383.462.082.674.754.831.677.3313.671978-04-01419781
1978-05-0110.5412.219.085.2911.0010.0811.1713.7511.8711.7912.8727.161978-05-01519781
1978-06-0110.3711.426.466.0411.257.506.465.967.795.465.5010.411978-06-01619781
1978-07-0112.4610.6311.176.7512.929.0412.429.6212.088.0414.0416.171978-07-01719781
1978-08-0119.3315.0920.178.8312.6210.419.3312.339.509.9215.7518.001978-08-01819781
1978-09-018.426.139.875.253.215.717.253.507.336.507.6215.961978-09-01919781
1978-10-019.506.8310.503.886.134.584.216.506.386.5410.6314.091978-10-011019781
1978-11-0113.5916.7511.257.0811.048.338.1711.2910.7511.2523.1325.001978-11-011119781
1978-12-0121.2916.2924.0412.7918.2119.2921.5417.2116.7117.8317.7525.701978-12-011219781
\n", "

216 rows × 16 columns

\n", "
" ], "text/plain": [ " RPT VAL ROS KIL SHA BIR DUB CLA MUL \\\n", "Yr_Mo_Dy \n", "1961-01-01 15.04 14.96 13.17 9.29 NaN 9.87 13.67 10.25 10.83 \n", "1961-02-01 14.25 15.12 9.04 5.88 12.08 7.17 10.17 3.63 6.50 \n", "1961-03-01 12.67 13.13 11.79 6.42 9.79 8.54 10.25 13.29 NaN \n", "1961-04-01 8.38 6.34 8.33 6.75 9.33 9.54 11.67 8.21 11.21 \n", "1961-05-01 15.87 13.88 15.37 9.79 13.46 10.17 9.96 14.04 9.75 \n", "1961-06-01 15.92 9.59 12.04 8.79 11.54 6.04 9.75 8.29 9.33 \n", "1961-07-01 7.21 6.83 7.71 4.42 8.46 4.79 6.71 6.00 5.79 \n", "1961-08-01 9.59 5.09 5.54 4.63 8.29 5.25 4.21 5.25 5.37 \n", "1961-09-01 5.58 1.13 4.96 3.04 4.25 2.25 4.63 2.71 3.67 \n", "1961-10-01 14.25 12.87 7.87 8.00 13.00 7.75 5.83 9.00 7.08 \n", "1961-11-01 13.21 13.13 14.33 8.54 12.17 10.21 13.08 12.17 10.92 \n", "1961-12-01 9.67 7.75 8.00 3.96 6.00 2.75 7.25 2.50 5.58 \n", "1962-01-01 9.29 3.42 11.54 3.50 2.21 1.96 10.41 2.79 3.54 \n", "1962-02-01 19.12 13.96 12.21 10.58 15.71 10.63 15.71 11.08 13.17 \n", "1962-03-01 8.21 4.83 9.00 4.83 6.00 2.21 7.96 1.87 4.08 \n", "1962-04-01 14.33 12.25 11.87 10.37 14.92 11.00 19.79 11.67 14.09 \n", "1962-05-01 9.62 9.54 3.58 3.33 8.75 3.75 2.25 2.58 1.67 \n", "1962-06-01 5.88 6.29 8.67 5.21 5.00 4.25 5.91 5.41 4.79 \n", "1962-07-01 8.67 4.17 6.92 6.71 8.17 5.66 11.17 9.38 8.75 \n", "1962-08-01 4.58 5.37 6.04 2.29 7.87 3.71 4.46 2.58 4.00 \n", "1962-09-01 10.00 12.08 10.96 9.25 9.29 7.62 7.41 8.75 7.67 \n", "1962-10-01 14.58 7.83 19.21 10.08 11.54 8.38 13.29 10.63 8.21 \n", "1962-11-01 16.88 13.25 16.00 8.96 13.46 11.46 10.46 10.17 10.37 \n", "1962-12-01 18.38 15.41 11.75 6.79 12.21 8.04 8.42 10.83 5.66 \n", "1963-01-01 15.59 13.62 19.79 8.38 12.25 10.00 23.45 15.71 13.59 \n", "1963-02-01 15.41 7.62 24.67 11.42 9.21 8.17 14.04 7.54 7.54 \n", "1963-03-01 16.75 19.67 17.67 8.87 19.08 15.37 16.21 14.29 11.29 \n", "1963-04-01 10.54 9.59 12.46 7.33 9.46 9.59 11.79 11.87 9.79 \n", "1963-05-01 18.79 14.17 13.59 11.63 14.17 11.96 14.46 12.46 12.87 \n", "1963-06-01 13.37 6.87 12.00 8.50 10.04 9.42 10.92 12.96 11.79 \n", "... ... ... ... ... ... ... ... ... ... \n", "1976-07-01 8.50 1.75 6.58 2.13 2.75 2.21 5.37 2.04 5.88 \n", "1976-08-01 13.00 8.38 8.63 5.83 12.92 8.25 13.00 9.42 10.58 \n", "1976-09-01 11.87 11.00 7.38 6.87 7.75 8.33 10.34 6.46 10.17 \n", "1976-10-01 10.96 6.71 10.41 4.63 7.58 5.04 5.04 5.54 6.50 \n", "1976-11-01 13.96 15.67 10.29 6.46 12.79 9.08 10.00 9.67 10.21 \n", "1976-12-01 13.46 16.42 9.21 4.54 10.75 8.67 10.88 4.83 8.79 \n", "1977-01-01 20.04 11.92 20.25 9.13 9.29 8.04 10.75 5.88 9.00 \n", "1977-02-01 11.83 9.71 11.00 4.25 8.58 8.71 6.17 5.66 8.29 \n", "1977-03-01 8.63 14.83 10.29 3.75 6.63 8.79 5.00 8.12 7.87 \n", "1977-04-01 21.67 16.00 17.33 13.59 20.83 15.96 25.62 17.62 19.41 \n", "1977-05-01 6.42 7.12 8.67 3.58 4.58 4.00 6.75 6.13 3.33 \n", "1977-06-01 7.08 5.25 9.71 2.83 2.21 3.50 5.29 1.42 2.00 \n", "1977-07-01 15.41 16.29 17.08 6.25 11.83 11.83 12.29 10.58 10.41 \n", "1977-08-01 4.33 2.96 4.42 2.33 0.96 1.08 4.96 1.87 2.33 \n", "1977-09-01 17.37 16.33 16.83 8.58 14.46 11.83 15.09 13.92 13.29 \n", "1977-10-01 16.75 15.34 12.25 9.42 16.38 11.38 18.50 13.92 14.09 \n", "1977-11-01 16.71 11.54 12.17 4.17 8.54 7.17 11.12 6.46 8.25 \n", "1977-12-01 13.37 10.92 12.42 2.37 5.79 6.13 8.96 7.38 6.29 \n", "1978-01-01 8.33 7.12 7.71 3.54 8.50 7.50 14.71 10.00 11.83 \n", "1978-02-01 27.25 24.21 18.16 17.46 27.54 18.05 20.96 25.04 20.04 \n", "1978-03-01 15.04 6.21 16.04 7.87 6.42 6.67 12.29 8.00 10.58 \n", "1978-04-01 3.42 7.58 2.71 1.38 3.46 2.08 2.67 4.75 4.83 \n", "1978-05-01 10.54 12.21 9.08 5.29 11.00 10.08 11.17 13.75 11.87 \n", "1978-06-01 10.37 11.42 6.46 6.04 11.25 7.50 6.46 5.96 7.79 \n", "1978-07-01 12.46 10.63 11.17 6.75 12.92 9.04 12.42 9.62 12.08 \n", "1978-08-01 19.33 15.09 20.17 8.83 12.62 10.41 9.33 12.33 9.50 \n", "1978-09-01 8.42 6.13 9.87 5.25 3.21 5.71 7.25 3.50 7.33 \n", "1978-10-01 9.50 6.83 10.50 3.88 6.13 4.58 4.21 6.50 6.38 \n", "1978-11-01 13.59 16.75 11.25 7.08 11.04 8.33 8.17 11.29 10.75 \n", "1978-12-01 21.29 16.29 24.04 12.79 18.21 19.29 21.54 17.21 16.71 \n", "\n", " CLO BEL MAL date month year day \n", "Yr_Mo_Dy \n", "1961-01-01 12.58 18.50 15.04 1961-01-01 1 1961 1 \n", "1961-02-01 5.50 9.17 8.00 1961-02-01 2 1961 1 \n", "1961-03-01 12.21 20.62 NaN 1961-03-01 3 1961 1 \n", "1961-04-01 6.46 11.96 7.17 1961-04-01 4 1961 1 \n", "1961-05-01 9.92 18.63 11.12 1961-05-01 5 1961 1 \n", "1961-06-01 10.34 10.67 12.12 1961-06-01 6 1961 1 \n", "1961-07-01 7.96 6.96 8.71 1961-07-01 7 1961 1 \n", "1961-08-01 5.41 8.38 9.08 1961-08-01 8 1961 1 \n", "1961-09-01 6.00 4.79 5.41 1961-09-01 9 1961 1 \n", "1961-10-01 5.29 11.79 4.04 1961-10-01 10 1961 1 \n", "1961-11-01 13.54 20.17 20.04 1961-11-01 11 1961 1 \n", "1961-12-01 5.58 7.79 11.17 1961-12-01 12 1961 1 \n", "1962-01-01 5.17 4.38 7.92 1962-01-01 1 1962 1 \n", "1962-02-01 12.62 17.67 22.71 1962-02-01 2 1962 1 \n", "1962-03-01 3.92 4.08 5.41 1962-03-01 3 1962 1 \n", "1962-04-01 15.46 16.62 23.58 1962-04-01 4 1962 1 \n", "1962-05-01 2.37 7.29 3.25 1962-05-01 5 1962 1 \n", "1962-06-01 9.25 5.25 10.71 1962-06-01 6 1962 1 \n", "1962-07-01 11.12 10.25 17.08 1962-07-01 7 1962 1 \n", "1962-08-01 4.79 7.21 7.46 1962-08-01 8 1962 1 \n", "1962-09-01 9.62 14.58 11.92 1962-09-01 9 1962 1 \n", "1962-10-01 12.92 18.05 18.12 1962-10-01 10 1962 1 \n", "1962-11-01 13.21 14.83 15.16 1962-11-01 11 1962 1 \n", "1962-12-01 9.08 11.50 11.50 1962-12-01 12 1962 1 \n", "1963-01-01 14.37 17.58 34.13 1963-01-01 1 1963 1 \n", "1963-02-01 10.08 10.17 17.67 1963-02-01 2 1963 1 \n", "1963-03-01 9.21 19.92 19.79 1963-03-01 3 1963 1 \n", "1963-04-01 10.71 13.37 18.21 1963-04-01 4 1963 1 \n", "1963-05-01 13.96 15.29 21.62 1963-05-01 5 1963 1 \n", "1963-06-01 11.04 10.92 13.67 1963-06-01 6 1963 1 \n", "... ... ... ... ... ... ... ... \n", "1976-07-01 4.50 4.96 10.63 1976-07-01 7 1976 1 \n", "1976-08-01 11.34 14.21 20.25 1976-08-01 8 1976 1 \n", "1976-09-01 9.29 12.75 19.55 1976-09-01 9 1976 1 \n", "1976-10-01 3.92 6.79 5.00 1976-10-01 10 1976 1 \n", "1976-11-01 11.63 23.09 21.96 1976-11-01 11 1976 1 \n", "1976-12-01 5.91 8.83 13.67 1976-12-01 12 1976 1 \n", "1977-01-01 9.00 14.88 25.70 1977-01-01 1 1977 1 \n", "1977-02-01 7.58 11.71 16.50 1977-02-01 2 1977 1 \n", "1977-03-01 6.42 13.54 13.67 1977-03-01 3 1977 1 \n", "1977-04-01 20.67 24.37 30.09 1977-04-01 4 1977 1 \n", "1977-05-01 4.50 19.21 12.38 1977-05-01 5 1977 1 \n", "1977-06-01 0.92 5.21 5.63 1977-06-01 6 1977 1 \n", "1977-07-01 7.21 17.37 7.83 1977-07-01 7 1977 1 \n", "1977-08-01 2.04 10.50 9.83 1977-08-01 8 1977 1 \n", "1977-09-01 13.88 23.29 25.17 1977-09-01 9 1977 1 \n", "1977-10-01 14.46 22.34 29.67 1977-10-01 10 1977 1 \n", "1977-11-01 6.21 11.04 15.63 1977-11-01 11 1977 1 \n", "1977-12-01 5.71 8.54 12.42 1977-12-01 12 1977 1 \n", "1978-01-01 10.00 15.09 20.46 1978-01-01 1 1978 1 \n", "1978-02-01 17.50 27.71 21.12 1978-02-01 2 1978 1 \n", "1978-03-01 9.33 5.41 17.00 1978-03-01 3 1978 1 \n", "1978-04-01 1.67 7.33 13.67 1978-04-01 4 1978 1 \n", "1978-05-01 11.79 12.87 27.16 1978-05-01 5 1978 1 \n", "1978-06-01 5.46 5.50 10.41 1978-06-01 6 1978 1 \n", "1978-07-01 8.04 14.04 16.17 1978-07-01 7 1978 1 \n", "1978-08-01 9.92 15.75 18.00 1978-08-01 8 1978 1 \n", "1978-09-01 6.50 7.62 15.96 1978-09-01 9 1978 1 \n", "1978-10-01 6.54 10.63 14.09 1978-10-01 10 1978 1 \n", "1978-11-01 11.25 23.13 25.00 1978-11-01 11 1978 1 \n", "1978-12-01 17.83 17.75 25.70 1978-12-01 12 1978 1 \n", "\n", "[216 rows x 16 columns]" ] }, "execution_count": 429, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.query('day == 1')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 14. Downsample the record to a weekly frequency for each location." ] }, { "cell_type": "code", "execution_count": 430, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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RPTVALROSKILSHABIRDUBCLAMULCLOBELMALdatemonthyearday
Yr_Mo_Dy
1961-01-0115.0414.9613.179.29NaN9.8713.6710.2510.8312.5818.5015.041961-01-01119611
1961-01-0810.969.757.625.919.627.2914.297.629.2510.4616.6216.461961-01-08119618
1961-01-1512.049.6711.752.377.383.132.506.834.755.637.546.751961-01-151196115
1961-01-229.595.889.922.176.875.509.387.046.347.5010.889.921961-01-221196122
1961-01-29NaN23.9122.2917.5424.0819.7022.0020.2521.4619.9527.7123.381961-01-291196129
\n", "
" ], "text/plain": [ " RPT VAL ROS KIL SHA BIR DUB CLA MUL \\\n", "Yr_Mo_Dy \n", "1961-01-01 15.04 14.96 13.17 9.29 NaN 9.87 13.67 10.25 10.83 \n", "1961-01-08 10.96 9.75 7.62 5.91 9.62 7.29 14.29 7.62 9.25 \n", "1961-01-15 12.04 9.67 11.75 2.37 7.38 3.13 2.50 6.83 4.75 \n", "1961-01-22 9.59 5.88 9.92 2.17 6.87 5.50 9.38 7.04 6.34 \n", "1961-01-29 NaN 23.91 22.29 17.54 24.08 19.70 22.00 20.25 21.46 \n", "\n", " CLO BEL MAL date month year day \n", "Yr_Mo_Dy \n", "1961-01-01 12.58 18.50 15.04 1961-01-01 1 1961 1 \n", "1961-01-08 10.46 16.62 16.46 1961-01-08 1 1961 8 \n", "1961-01-15 5.63 7.54 6.75 1961-01-15 1 1961 15 \n", "1961-01-22 7.50 10.88 9.92 1961-01-22 1 1961 22 \n", "1961-01-29 19.95 27.71 23.38 1961-01-29 1 1961 29 " ] }, "execution_count": 430, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[::7].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 15. Calculate the mean windspeed for each month in the dataset. \n", "#### Treat January 1961 and January 1962 as *different* months.\n", "#### (hint: first find a way to create an identifier unique for each month.)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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YrMoDyRPTVALROSKILSHABIRDUBCLAMULCLOBELMALmonths_num
0611115.0414.9613.179.29NaN9.8713.6710.2510.8312.5818.5015.041
1611214.71NaN10.836.5012.627.6711.5010.049.799.6717.5413.831
2611318.5016.8812.3310.1311.176.1711.25NaN8.507.6712.7512.711
3611410.586.6311.754.584.542.888.631.795.835.885.4610.881
4611513.3313.2511.426.1710.718.2111.926.5410.9210.3412.9211.831
5611613.218.129.966.675.374.5010.674.427.177.508.1213.171
6611713.5014.299.504.9612.298.339.179.297.587.9613.9613.791
7611810.969.757.625.919.627.2914.297.629.2510.4616.6216.461
8611912.5810.8310.004.7510.376.798.0410.137.799.0813.0415.371
96111013.3711.1219.508.339.716.5411.427.798.549.008.5811.831
106111110.589.878.422.798.717.257.548.335.718.6720.7116.921
116111219.7512.0818.5010.5410.299.4615.5411.5010.3714.5815.5914.091
12611139.923.548.462.962.290.964.630.582.333.375.257.041
13611149.045.667.080.672.711.383.082.580.502.677.175.171
146111512.049.6711.752.377.383.132.506.834.755.637.546.751
156111616.4211.2515.674.7111.346.929.258.798.217.3313.049.041
166111717.7514.3717.3310.1313.9613.3713.4211.048.7111.3815.9216.081
176111819.8312.0420.7918.54NaN10.2917.8311.3814.6716.718.7917.961
18611194.923.427.291.043.673.173.712.791.922.716.877.831
19611209.5911.837.961.587.925.003.174.923.133.376.506.791
206112114.3310.2511.926.1310.047.678.049.177.047.876.7512.421
21611229.595.889.922.176.875.509.387.046.347.5010.889.921
226112316.549.9618.5410.4613.5012.6713.7013.7510.7513.1714.7920.581
236112425.0414.8325.8415.6721.4618.5820.3819.3815.3715.1223.0925.251
246112513.6211.1712.676.0410.009.429.258.717.1212.0414.0417.501
256112624.3718.7917.5014.2518.9115.6714.3315.1616.0819.0820.5025.251
266112722.0420.7917.4116.2119.0416.1316.7918.2918.6619.0826.0827.631
276112817.6713.5413.338.8715.0411.6312.2510.5811.9211.0420.3018.121
2861129NaN23.9122.2917.5424.0819.7022.0020.2521.4619.9527.7123.381
296113012.2111.4210.927.9213.089.6214.5010.219.9211.9618.8819.251
...................................................
65447812213.7012.7114.295.139.218.0412.336.349.2111.219.5919.95216
65457812321.2121.3417.7511.5816.7514.4617.4615.2915.7917.5021.4225.75216
6546781249.9213.507.211.7111.007.508.387.4610.7910.2117.8817.96216
65477812522.7520.1718.588.5015.9614.2913.9212.9212.9612.2917.0819.83216
65487812629.3323.8725.3716.0424.4619.5024.5418.5821.0020.5821.6734.46216
65497812726.6324.7924.7918.1623.1319.5819.9219.0419.7521.5023.0434.59216
65507812812.9212.5411.253.376.505.9610.346.176.636.759.5417.33216
65517812918.7116.9215.506.0410.379.5910.759.139.7511.0814.3315.34216
655278121024.9222.5416.5414.6215.5913.0013.2114.1216.2116.1726.0821.92216
655378121120.2519.1717.8311.6317.7913.3714.8313.8815.5416.2918.3422.83216
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655778121513.138.9216.546.926.004.0012.675.887.676.085.5017.16216
655878121614.889.1317.375.216.712.469.134.966.135.9610.9218.08216
65597812179.873.218.042.213.040.542.461.461.292.675.009.08216
65607812189.8310.888.501.009.086.002.428.254.425.8819.7919.79216
656178121913.8811.4210.132.338.126.754.755.886.218.178.3318.25216
65627812209.923.6312.383.083.500.424.542.502.134.713.2110.29216
656378122112.963.8313.794.797.126.5411.679.258.679.0011.2520.30216
65647812226.217.3813.082.547.585.332.468.385.095.049.9211.00216
656578122316.6213.2922.219.5014.2913.0816.5017.1612.7112.0018.5021.50216
65667812248.675.6312.124.795.095.9112.259.2510.8311.7111.9231.71216
65677812257.216.587.832.674.794.588.710.755.215.251.2113.96216
656878122613.8311.8710.342.376.964.291.963.793.043.084.7911.96216
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6574 rows × 16 columns

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" ], "text/plain": [ " Yr Mo Dy RPT VAL ROS KIL SHA BIR DUB CLA \\\n", "0 61 1 1 15.04 14.96 13.17 9.29 NaN 9.87 13.67 10.25 \n", "1 61 1 2 14.71 NaN 10.83 6.50 12.62 7.67 11.50 10.04 \n", "2 61 1 3 18.50 16.88 12.33 10.13 11.17 6.17 11.25 NaN \n", "3 61 1 4 10.58 6.63 11.75 4.58 4.54 2.88 8.63 1.79 \n", "4 61 1 5 13.33 13.25 11.42 6.17 10.71 8.21 11.92 6.54 \n", "5 61 1 6 13.21 8.12 9.96 6.67 5.37 4.50 10.67 4.42 \n", "6 61 1 7 13.50 14.29 9.50 4.96 12.29 8.33 9.17 9.29 \n", "7 61 1 8 10.96 9.75 7.62 5.91 9.62 7.29 14.29 7.62 \n", "8 61 1 9 12.58 10.83 10.00 4.75 10.37 6.79 8.04 10.13 \n", "9 61 1 10 13.37 11.12 19.50 8.33 9.71 6.54 11.42 7.79 \n", "10 61 1 11 10.58 9.87 8.42 2.79 8.71 7.25 7.54 8.33 \n", "11 61 1 12 19.75 12.08 18.50 10.54 10.29 9.46 15.54 11.50 \n", "12 61 1 13 9.92 3.54 8.46 2.96 2.29 0.96 4.63 0.58 \n", "13 61 1 14 9.04 5.66 7.08 0.67 2.71 1.38 3.08 2.58 \n", "14 61 1 15 12.04 9.67 11.75 2.37 7.38 3.13 2.50 6.83 \n", "15 61 1 16 16.42 11.25 15.67 4.71 11.34 6.92 9.25 8.79 \n", "16 61 1 17 17.75 14.37 17.33 10.13 13.96 13.37 13.42 11.04 \n", "17 61 1 18 19.83 12.04 20.79 18.54 NaN 10.29 17.83 11.38 \n", "18 61 1 19 4.92 3.42 7.29 1.04 3.67 3.17 3.71 2.79 \n", "19 61 1 20 9.59 11.83 7.96 1.58 7.92 5.00 3.17 4.92 \n", "20 61 1 21 14.33 10.25 11.92 6.13 10.04 7.67 8.04 9.17 \n", "21 61 1 22 9.59 5.88 9.92 2.17 6.87 5.50 9.38 7.04 \n", "22 61 1 23 16.54 9.96 18.54 10.46 13.50 12.67 13.70 13.75 \n", "23 61 1 24 25.04 14.83 25.84 15.67 21.46 18.58 20.38 19.38 \n", "24 61 1 25 13.62 11.17 12.67 6.04 10.00 9.42 9.25 8.71 \n", "25 61 1 26 24.37 18.79 17.50 14.25 18.91 15.67 14.33 15.16 \n", "26 61 1 27 22.04 20.79 17.41 16.21 19.04 16.13 16.79 18.29 \n", "27 61 1 28 17.67 13.54 13.33 8.87 15.04 11.63 12.25 10.58 \n", "28 61 1 29 NaN 23.91 22.29 17.54 24.08 19.70 22.00 20.25 \n", "29 61 1 30 12.21 11.42 10.92 7.92 13.08 9.62 14.50 10.21 \n", "... .. .. .. ... ... ... ... ... ... ... ... \n", "6544 78 12 2 13.70 12.71 14.29 5.13 9.21 8.04 12.33 6.34 \n", "6545 78 12 3 21.21 21.34 17.75 11.58 16.75 14.46 17.46 15.29 \n", "6546 78 12 4 9.92 13.50 7.21 1.71 11.00 7.50 8.38 7.46 \n", "6547 78 12 5 22.75 20.17 18.58 8.50 15.96 14.29 13.92 12.92 \n", "6548 78 12 6 29.33 23.87 25.37 16.04 24.46 19.50 24.54 18.58 \n", "6549 78 12 7 26.63 24.79 24.79 18.16 23.13 19.58 19.92 19.04 \n", "6550 78 12 8 12.92 12.54 11.25 3.37 6.50 5.96 10.34 6.17 \n", "6551 78 12 9 18.71 16.92 15.50 6.04 10.37 9.59 10.75 9.13 \n", "6552 78 12 10 24.92 22.54 16.54 14.62 15.59 13.00 13.21 14.12 \n", "6553 78 12 11 20.25 19.17 17.83 11.63 17.79 13.37 14.83 13.88 \n", "6554 78 12 12 23.13 18.63 18.05 8.29 14.33 11.04 10.54 10.13 \n", "6555 78 12 13 18.84 24.04 14.37 8.33 18.12 12.17 13.00 13.75 \n", "6556 78 12 14 17.21 19.75 12.71 5.83 13.79 7.33 8.83 5.71 \n", "6557 78 12 15 13.13 8.92 16.54 6.92 6.00 4.00 12.67 5.88 \n", "6558 78 12 16 14.88 9.13 17.37 5.21 6.71 2.46 9.13 4.96 \n", "6559 78 12 17 9.87 3.21 8.04 2.21 3.04 0.54 2.46 1.46 \n", "6560 78 12 18 9.83 10.88 8.50 1.00 9.08 6.00 2.42 8.25 \n", "6561 78 12 19 13.88 11.42 10.13 2.33 8.12 6.75 4.75 5.88 \n", "6562 78 12 20 9.92 3.63 12.38 3.08 3.50 0.42 4.54 2.50 \n", "6563 78 12 21 12.96 3.83 13.79 4.79 7.12 6.54 11.67 9.25 \n", "6564 78 12 22 6.21 7.38 13.08 2.54 7.58 5.33 2.46 8.38 \n", "6565 78 12 23 16.62 13.29 22.21 9.50 14.29 13.08 16.50 17.16 \n", "6566 78 12 24 8.67 5.63 12.12 4.79 5.09 5.91 12.25 9.25 \n", "6567 78 12 25 7.21 6.58 7.83 2.67 4.79 4.58 8.71 0.75 \n", "6568 78 12 26 13.83 11.87 10.34 2.37 6.96 4.29 1.96 3.79 \n", "6569 78 12 27 17.58 16.96 17.62 8.08 13.21 11.67 14.46 15.59 \n", "6570 78 12 28 13.21 5.46 13.46 5.00 8.12 9.42 14.33 16.25 \n", "6571 78 12 29 14.00 10.29 14.42 8.71 9.71 10.54 19.17 12.46 \n", "6572 78 12 30 18.50 14.04 21.29 9.13 12.75 9.71 18.08 12.87 \n", "6573 78 12 31 20.33 17.41 27.29 9.59 12.08 10.13 19.25 11.63 \n", "\n", " MUL CLO BEL MAL months_num \n", "0 10.83 12.58 18.50 15.04 1 \n", "1 9.79 9.67 17.54 13.83 1 \n", "2 8.50 7.67 12.75 12.71 1 \n", "3 5.83 5.88 5.46 10.88 1 \n", "4 10.92 10.34 12.92 11.83 1 \n", "5 7.17 7.50 8.12 13.17 1 \n", "6 7.58 7.96 13.96 13.79 1 \n", "7 9.25 10.46 16.62 16.46 1 \n", "8 7.79 9.08 13.04 15.37 1 \n", "9 8.54 9.00 8.58 11.83 1 \n", "10 5.71 8.67 20.71 16.92 1 \n", "11 10.37 14.58 15.59 14.09 1 \n", "12 2.33 3.37 5.25 7.04 1 \n", "13 0.50 2.67 7.17 5.17 1 \n", "14 4.75 5.63 7.54 6.75 1 \n", "15 8.21 7.33 13.04 9.04 1 \n", "16 8.71 11.38 15.92 16.08 1 \n", "17 14.67 16.71 8.79 17.96 1 \n", "18 1.92 2.71 6.87 7.83 1 \n", "19 3.13 3.37 6.50 6.79 1 \n", "20 7.04 7.87 6.75 12.42 1 \n", "21 6.34 7.50 10.88 9.92 1 \n", "22 10.75 13.17 14.79 20.58 1 \n", "23 15.37 15.12 23.09 25.25 1 \n", "24 7.12 12.04 14.04 17.50 1 \n", "25 16.08 19.08 20.50 25.25 1 \n", "26 18.66 19.08 26.08 27.63 1 \n", "27 11.92 11.04 20.30 18.12 1 \n", "28 21.46 19.95 27.71 23.38 1 \n", "29 9.92 11.96 18.88 19.25 1 \n", "... ... ... ... ... ... \n", "6544 9.21 11.21 9.59 19.95 216 \n", "6545 15.79 17.50 21.42 25.75 216 \n", "6546 10.79 10.21 17.88 17.96 216 \n", "6547 12.96 12.29 17.08 19.83 216 \n", "6548 21.00 20.58 21.67 34.46 216 \n", "6549 19.75 21.50 23.04 34.59 216 \n", "6550 6.63 6.75 9.54 17.33 216 \n", "6551 9.75 11.08 14.33 15.34 216 \n", "6552 16.21 16.17 26.08 21.92 216 \n", "6553 15.54 16.29 18.34 22.83 216 \n", "6554 11.42 10.50 11.25 13.50 216 \n", "6555 14.17 15.09 21.50 21.37 216 \n", "6556 7.96 3.37 5.21 6.92 216 \n", "6557 7.67 6.08 5.50 17.16 216 \n", "6558 6.13 5.96 10.92 18.08 216 \n", "6559 1.29 2.67 5.00 9.08 216 \n", "6560 4.42 5.88 19.79 19.79 216 \n", "6561 6.21 8.17 8.33 18.25 216 \n", "6562 2.13 4.71 3.21 10.29 216 \n", "6563 8.67 9.00 11.25 20.30 216 \n", "6564 5.09 5.04 9.92 11.00 216 \n", "6565 12.71 12.00 18.50 21.50 216 \n", "6566 10.83 11.71 11.92 31.71 216 \n", "6567 5.21 5.25 1.21 13.96 216 \n", "6568 3.04 3.08 4.79 11.96 216 \n", "6569 14.04 14.00 17.21 40.08 216 \n", "6570 15.25 18.05 21.79 41.46 216 \n", "6571 14.50 16.42 18.88 29.58 216 \n", "6572 12.46 12.12 14.67 28.79 216 \n", "6573 11.58 11.38 12.08 22.08 216 \n", "\n", "[6574 rows x 16 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# call data again but this time don't use parse_dates\n", "wind_data = pd.read_table(\"wind.data\", sep = \"\\s+\") \n", "\n", "# compute the month number for each day in the dataset, there are in total 216 months\n", "wind_data['months_num'] = (wind_data.iloc[:, 0] - 61) * 12 + wind_data.iloc[:, 1]\n", "\n", "wind_data\n", "\n", "# group the data according to the months_num and get the mean\n", "# monthly_data = wind_data.groupby(['months_num']).mean()\n", "\n", "# monthly_data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 16. Calculate the min, max and mean windspeeds and standard deviations of the windspeeds across all locations for each week (assume that the first week starts on January 2 1961) for the first 52 weeks." ] }, { "cell_type": "code", "execution_count": 433, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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RPTVALROS...CLOBELMAL
minmaxmeanstdminmaxmeanstdminmax...meanstdminmaxmeanstdminmaxmeanstd
Yr_Mo_Dy
1961-01-0810.5818.5013.5414292.6313216.6316.8811.4866673.9495257.6212.33...8.4971431.7049415.4617.5412.4814294.34913910.8816.4613.2385711.773062
1961-01-159.0419.7512.4685713.5553923.5412.088.9671433.1489457.0819.50...7.5714294.0842935.2520.7111.1257145.5522155.1716.9211.0242864.692355
1961-01-224.9219.8313.2042865.3374023.4214.379.8628573.8377857.2920.79...8.1242864.7839526.5015.929.8214293.6265846.7917.9611.4342864.237239
1961-01-2913.6225.0419.8800004.6190619.9623.9116.1414295.17022412.6725.84...15.6400003.71336814.0427.7120.9300005.21072617.5027.6322.5300003.874721
1961-02-0510.5824.2116.8271435.2514089.4624.2115.4600005.1873959.0419.70...9.4600002.8395019.1719.3314.0128574.2108587.1719.2511.9357144.336104
1961-02-1216.0024.5419.6842863.58767711.5421.4216.4171433.60837313.6721.34...14.4400001.74674915.2126.3821.8328574.06375317.0421.8419.1557141.828705
1961-02-196.0422.5015.1300005.06460911.6320.1715.0914293.5750126.1319.41...13.5428572.53136114.0929.6321.1671435.91093810.9622.5816.5842864.685377
1961-02-267.7925.8015.2214297.0207167.0821.5013.6257145.1473486.0822.42...12.7300004.9200649.5923.2116.3042865.0911626.6723.8714.3228576.182283
1961-03-0510.9613.3312.1014290.9977218.8317.0012.9514292.8519558.1713.67...12.3700001.59368511.5823.4517.8428574.3323318.8317.5413.9516673.021387
1961-03-124.8814.799.3766673.7322638.0816.9611.5785713.2301677.5416.38...10.4585713.65511310.2122.7116.7014294.3587595.5422.5414.4200005.769890
\n", "

10 rows × 48 columns

\n", "
" ], "text/plain": [ " RPT VAL \\\n", " min max mean std min max mean \n", "Yr_Mo_Dy \n", "1961-01-08 10.58 18.50 13.541429 2.631321 6.63 16.88 11.486667 \n", "1961-01-15 9.04 19.75 12.468571 3.555392 3.54 12.08 8.967143 \n", "1961-01-22 4.92 19.83 13.204286 5.337402 3.42 14.37 9.862857 \n", "1961-01-29 13.62 25.04 19.880000 4.619061 9.96 23.91 16.141429 \n", "1961-02-05 10.58 24.21 16.827143 5.251408 9.46 24.21 15.460000 \n", "1961-02-12 16.00 24.54 19.684286 3.587677 11.54 21.42 16.417143 \n", "1961-02-19 6.04 22.50 15.130000 5.064609 11.63 20.17 15.091429 \n", "1961-02-26 7.79 25.80 15.221429 7.020716 7.08 21.50 13.625714 \n", "1961-03-05 10.96 13.33 12.101429 0.997721 8.83 17.00 12.951429 \n", "1961-03-12 4.88 14.79 9.376667 3.732263 8.08 16.96 11.578571 \n", "\n", " ROS ... CLO BEL \\\n", " std min max ... mean std min \n", "Yr_Mo_Dy ... \n", "1961-01-08 3.949525 7.62 12.33 ... 8.497143 1.704941 5.46 \n", "1961-01-15 3.148945 7.08 19.50 ... 7.571429 4.084293 5.25 \n", "1961-01-22 3.837785 7.29 20.79 ... 8.124286 4.783952 6.50 \n", "1961-01-29 5.170224 12.67 25.84 ... 15.640000 3.713368 14.04 \n", "1961-02-05 5.187395 9.04 19.70 ... 9.460000 2.839501 9.17 \n", "1961-02-12 3.608373 13.67 21.34 ... 14.440000 1.746749 15.21 \n", "1961-02-19 3.575012 6.13 19.41 ... 13.542857 2.531361 14.09 \n", "1961-02-26 5.147348 6.08 22.42 ... 12.730000 4.920064 9.59 \n", "1961-03-05 2.851955 8.17 13.67 ... 12.370000 1.593685 11.58 \n", "1961-03-12 3.230167 7.54 16.38 ... 10.458571 3.655113 10.21 \n", "\n", " MAL \n", " max mean std min max mean std \n", "Yr_Mo_Dy \n", "1961-01-08 17.54 12.481429 4.349139 10.88 16.46 13.238571 1.773062 \n", "1961-01-15 20.71 11.125714 5.552215 5.17 16.92 11.024286 4.692355 \n", "1961-01-22 15.92 9.821429 3.626584 6.79 17.96 11.434286 4.237239 \n", "1961-01-29 27.71 20.930000 5.210726 17.50 27.63 22.530000 3.874721 \n", "1961-02-05 19.33 14.012857 4.210858 7.17 19.25 11.935714 4.336104 \n", "1961-02-12 26.38 21.832857 4.063753 17.04 21.84 19.155714 1.828705 \n", "1961-02-19 29.63 21.167143 5.910938 10.96 22.58 16.584286 4.685377 \n", "1961-02-26 23.21 16.304286 5.091162 6.67 23.87 14.322857 6.182283 \n", "1961-03-05 23.45 17.842857 4.332331 8.83 17.54 13.951667 3.021387 \n", "1961-03-12 22.71 16.701429 4.358759 5.54 22.54 14.420000 5.769890 \n", "\n", "[10 rows x 48 columns]" ] }, "execution_count": 433, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# resample data to 'W' week and use the functions\n", "weekly = data.resample('W').agg(['min','max','mean','std'])\n", "\n", "# slice it for the first 52 weeks and locations\n", "weekly.ix[1:53, \"RPT\":\"MAL\"].head(10)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }