{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise notebook 2: Cleaning up our act\n", "This Jupyter notebook, for the second part of The Open University's _Learn to code for Data Analysis_ course, has code examples and coding activities for you. Remember to run the code in this notebook before you start.\n", "\n", "You'll come across steps in the course directing you to this notebook. Once you've done each exercise, go back to the corresponding step and mark it as complete. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import warnings\n", "warnings.simplefilter('ignore', FutureWarning)\n", "\n", "from pandas import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 1: Dataframes and CSV files\n", "\n", "To read a CSV file into a dataframe you need to call the pandas function called read_csv(). The simplest usage of this function is with a single argument, a string that holds the name of the CSV file, for example." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = read_csv('WHO POP TB all.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dataframe attributes\n", "\n", "A dataframe attribute is like a variable that can only be accessed in the context of a dataframe. One such attribute is columns which holds a dataframe's column names." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Country', 'Population (1000s)', 'TB deaths'], dtype='object')" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dataframe rows\n", "A dataframe has a default integer index for its rows, which starts at zero 0. The `iloc` attribute can be used to obtain the row at the given index." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Country Afghanistan\n", "Population (1000s) 30552\n", "TB deaths 13000\n", "Name: 0, dtype: object" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[0] # first row, index 0" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Country Algeria\n", "Population (1000s) 39208\n", "TB deaths 5100\n", "Name: 2, dtype: object" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[2] # third row, index 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The head() method\n", "\n", "The `head()` method returns a dataframe with the first rows, as many as given in the argument. By default, if the argument is missing, it returns the first five rows." ] }, { "cell_type": "code", "execution_count": 6, "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", "
CountryPopulation (1000s)TB deaths
0Afghanistan3055213000.00
1Albania317320.00
2Algeria392085100.00
3Andorra790.26
4Angola214726900.00
\n", "
" ], "text/plain": [ " Country Population (1000s) TB deaths\n", "0 Afghanistan 30552 13000.00\n", "1 Albania 3173 20.00\n", "2 Algeria 39208 5100.00\n", "3 Andorra 79 0.26\n", "4 Angola 21472 6900.00" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head() # first five rows" ] }, { "cell_type": "code", "execution_count": 7, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
CountryPopulation (1000s)TB deaths
0Afghanistan3055213000.00
1Albania317320.00
2Algeria392085100.00
3Andorra790.26
4Angola214726900.00
5Antigua and Barbuda901.20
6Argentina41446570.00
\n", "
" ], "text/plain": [ " Country Population (1000s) TB deaths\n", "0 Afghanistan 30552 13000.00\n", "1 Albania 3173 20.00\n", "2 Algeria 39208 5100.00\n", "3 Andorra 79 0.26\n", "4 Angola 21472 6900.00\n", "5 Antigua and Barbuda 90 1.20\n", "6 Argentina 41446 570.00" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(7) # first seven rows" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The tail() method\n", "The tail() method is similar to the head() method. If no argument is used, the last five rows of the dataframe are returned, otherwise the number of rows returned is dependent on the argument." ] }, { "cell_type": "code", "execution_count": 8, "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", "
CountryPopulation (1000s)TB deaths
189Venezuela (Bolivarian Republic of)30405480.0
190Viet Nam9168017000.0
191Yemen24407990.0
192Zambia145393600.0
193Zimbabwe141505700.0
\n", "
" ], "text/plain": [ " Country Population (1000s) TB deaths\n", "189 Venezuela (Bolivarian Republic of) 30405 480.0\n", "190 Viet Nam 91680 17000.0\n", "191 Yemen 24407 990.0\n", "192 Zambia 14539 3600.0\n", "193 Zimbabwe 14150 5700.0" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.tail() # last five rows" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Selecting and displaying columns in a dataframe\n", "You found in Week 1 that you can select and display the values for a single dataframe column by puting the name of the column (in quotes) within square brackets immediately after the dataframe's name. However you can also select and display the values for multiple columns too.\n", "\n", "To get multiple columns you need to use a list. A list in Python is a number of items separated by commas within square brackets, for example `['Country', 'Population (1000s)']`. This list is then put within square brackets immediately after the dataframe's name. The resulting expression represents a new dataframe, just with those columns, and therefore any dataframe method can be applied to it, like for example `head()`:" ] }, { "cell_type": "code", "execution_count": 9, "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", "
CountryPopulation (1000s)
0Afghanistan30552
1Albania3173
2Algeria39208
3Andorra79
4Angola21472
\n", "
" ], "text/plain": [ " Country Population (1000s)\n", "0 Afghanistan 30552\n", "1 Albania 3173\n", "2 Algeria 39208\n", "3 Andorra 79\n", "4 Angola 21472" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[['Country', 'Population (1000s)']].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Applying methods to a dataframe column\n", "The `iloc` attribute and the head() and tail() methods discussed above can be used with single columns." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5100.0" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['TB deaths'].iloc[2] # third value of deaths column" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "189 30405\n", "190 91680\n", "191 24407\n", "192 14539\n", "193 14150\n", "Name: Population (1000s), dtype: int64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Population (1000s)'].tail() # last five values of population column " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tasks\n", "\n", "In the code cell below, write the code to get and display the 55th row in the dataframe df." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the code cell below write the code to display the first 10 rows of the dataframe df." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the code cell below, select and display the first eight rows from the 'Country' and 'TB deaths' columns." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Now go back to the course step and mark it complete.**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 2: Comparison operators\n", "Python has the following comparison operators:\n", "\n", " == (equals)\n", " != (not equal)\n", " < (less than)\n", " > (greater than)\n", " <= (less than or equal to)\n", " >= (greater than or equal to)\n", "\n", "The following code will get and display all the rows in `df` where it is `True` that the value in the `'Population (1000s)'` column is greater than `80000`." ] }, { "cell_type": "code", "execution_count": 12, "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", " \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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
CountryPopulation (1000s)TB deaths
13Bangladesh15659580000.0
23Brazil2003624400.0
36China139333741000.0
53Egypt82056550.0
58Ethiopia9410130000.0
65Germany82727300.0
77India1252140240000.0
78Indonesia24986664000.0
85Japan1271442100.0
109Mexico1223322200.0
124Nigeria173615160000.0
128Pakistan18214349000.0
134Philippines9839427000.0
141Russian Federation14283417000.0
185United States of America320051490.0
190Viet Nam9168017000.0
\n", "
" ], "text/plain": [ " Country Population (1000s) TB deaths\n", "13 Bangladesh 156595 80000.0\n", "23 Brazil 200362 4400.0\n", "36 China 1393337 41000.0\n", "53 Egypt 82056 550.0\n", "58 Ethiopia 94101 30000.0\n", "65 Germany 82727 300.0\n", "77 India 1252140 240000.0\n", "78 Indonesia 249866 64000.0\n", "85 Japan 127144 2100.0\n", "109 Mexico 122332 2200.0\n", "124 Nigeria 173615 160000.0\n", "128 Pakistan 182143 49000.0\n", "134 Philippines 98394 27000.0\n", "141 Russian Federation 142834 17000.0\n", "185 United States of America 320051 490.0\n", "190 Viet Nam 91680 17000.0" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df['Population (1000s)'] > 80000] " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Task\n", "In the code cell below write code to find all the rows in df where TB deaths exceed 10000." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Now go back to the course step and mark it complete.**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 3: Bitwise operators\n", "\n", "Pandas has two operators to make more complicated queries. Use the operator `&` (means 'and') to select rows where two conditions are both true. Use the operator `|` (means 'or') to select rows where at least one condition is true. Don't forget to put parentheses around _each_ comparison. For example, the following expression selects only countries with a population over 80 million inhabitants **and** with more that 10 thousand deaths." ] }, { "cell_type": "code", "execution_count": 13, "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", " \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", "
CountryPopulation (1000s)TB deaths
13Bangladesh15659580000.0
36China139333741000.0
58Ethiopia9410130000.0
77India1252140240000.0
78Indonesia24986664000.0
124Nigeria173615160000.0
128Pakistan18214349000.0
134Philippines9839427000.0
141Russian Federation14283417000.0
190Viet Nam9168017000.0
\n", "
" ], "text/plain": [ " Country Population (1000s) TB deaths\n", "13 Bangladesh 156595 80000.0\n", "36 China 1393337 41000.0\n", "58 Ethiopia 94101 30000.0\n", "77 India 1252140 240000.0\n", "78 Indonesia 249866 64000.0\n", "124 Nigeria 173615 160000.0\n", "128 Pakistan 182143 49000.0\n", "134 Philippines 98394 27000.0\n", "141 Russian Federation 142834 17000.0\n", "190 Viet Nam 91680 17000.0" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[(df['Population (1000s)'] > 80000) & (df['TB deaths'] > 10000)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If the same columns will be used repeatedly in the program, the code becomes more readable if written as follows:" ] }, { "cell_type": "code", "execution_count": 14, "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", " \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", "
CountryPopulation (1000s)TB deaths
13Bangladesh15659580000.0
36China139333741000.0
58Ethiopia9410130000.0
77India1252140240000.0
78Indonesia24986664000.0
124Nigeria173615160000.0
128Pakistan18214349000.0
134Philippines9839427000.0
141Russian Federation14283417000.0
190Viet Nam9168017000.0
\n", "
" ], "text/plain": [ " Country Population (1000s) TB deaths\n", "13 Bangladesh 156595 80000.0\n", "36 China 1393337 41000.0\n", "58 Ethiopia 94101 30000.0\n", "77 India 1252140 240000.0\n", "78 Indonesia 249866 64000.0\n", "124 Nigeria 173615 160000.0\n", "128 Pakistan 182143 49000.0\n", "134 Philippines 98394 27000.0\n", "141 Russian Federation 142834 17000.0\n", "190 Viet Nam 91680 17000.0" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "population = df['Population (1000s)']\n", "deaths = df['TB deaths']\n", "df[(population > 80000) & (deaths > 10000)] " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Task\n", "In the code cell below find all the countries where the Population (1000s) is **less than or equal to** 50000 **or** TB deaths are **greater than or equal to** 20000." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Now go back to the course step and mark it complete.**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 4: Display rows from dataframe\n", "\n", "You have downloaded the file London_2014.csv from our website, it can now be read into a dataframe." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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GMTMax TemperatureCMean TemperatureCMin TemperatureCDew PointCMeanDew PointCMin DewpointCMax HumidityMean HumidityMin Humidity...Max VisibilityKmMean VisibilityKmMin VisibilitykMMax Wind SpeedKm/hMean Wind SpeedKm/hMax Gust SpeedKm/hPrecipitationmmCloudCoverEventsWindDirDegrees<br />
02014-1-11186974948673...31112402666.09.914.0Rain186<br />
12014-1-21196964948160...31155322150.01.022.0Rain214<br />
22014-1-31186952947654...31120502969.07.112.0Rain-Thunderstorm219<br />
32014-1-4963862938565...31125352139.09.914.0Rain211<br />
42014-1-51271114-11008877...311453516NaN0.255.0Rain199<br />
\n", "

5 rows × 23 columns

\n", "
" ], "text/plain": [ " GMT Max TemperatureC Mean TemperatureC Min TemperatureC \\\n", "0 2014-1-1 11 8 6 \n", "1 2014-1-2 11 9 6 \n", "2 2014-1-3 11 8 6 \n", "3 2014-1-4 9 6 3 \n", "4 2014-1-5 12 7 1 \n", "\n", " Dew PointC MeanDew PointC Min DewpointC Max Humidity Mean Humidity \\\n", "0 9 7 4 94 86 \n", "1 9 6 4 94 81 \n", "2 9 5 2 94 76 \n", "3 8 6 2 93 85 \n", "4 11 4 -1 100 88 \n", "\n", " Min Humidity ... Max VisibilityKm Mean VisibilityKm \\\n", "0 73 ... 31 11 \n", "1 60 ... 31 15 \n", "2 54 ... 31 12 \n", "3 65 ... 31 12 \n", "4 77 ... 31 14 \n", "\n", " Min VisibilitykM Max Wind SpeedKm/h Mean Wind SpeedKm/h \\\n", "0 2 40 26 \n", "1 5 32 21 \n", "2 0 50 29 \n", "3 5 35 21 \n", "4 5 35 16 \n", "\n", " Max Gust SpeedKm/h Precipitationmm CloudCover Events \\\n", "0 66.0 9.91 4.0 Rain \n", "1 50.0 1.02 2.0 Rain \n", "2 69.0 7.11 2.0 Rain-Thunderstorm \n", "3 39.0 9.91 4.0 Rain \n", "4 NaN 0.25 5.0 Rain \n", "\n", " WindDirDegrees
\n", "0 186
\n", "1 214
\n", "2 219
\n", "3 211
\n", "4 199
\n", "\n", "[5 rows x 23 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "london = read_csv('London_2014.csv')\n", "london.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Removing initial spaces\n", "\n", "There are too many columns for the dataframe to fit horizontally in this notebook, but they can be displayed separately." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['GMT', 'Max TemperatureC', 'Mean TemperatureC', 'Min TemperatureC',\n", " 'Dew PointC', 'MeanDew PointC', 'Min DewpointC', 'Max Humidity',\n", " ' Mean Humidity', ' Min Humidity', ' Max Sea Level PressurehPa',\n", " ' Mean Sea Level PressurehPa', ' Min Sea Level PressurehPa',\n", " ' Max VisibilityKm', ' Mean VisibilityKm', ' Min VisibilitykM',\n", " ' Max Wind SpeedKm/h', ' Mean Wind SpeedKm/h', ' Max Gust SpeedKm/h',\n", " 'Precipitationmm', ' CloudCover', ' Events', 'WindDirDegrees
'],\n", " dtype='object')" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "london.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This shows that ' Max Wind SpeedKm/h' is prefixed by a space, as are other columm names such as ' Mean Humidity' and ' Max Sea Level PressurehPa'.\n", "\n", "The read_csv() function has interpreted spaces after commas as being part of the next value. This can be rectified easily by adding another argument to the read_csv() function to skip the initial spaces after a comma." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "london = read_csv('London_2014.csv', skipinitialspace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Removing extra characters\n", "\n", "Another problem shown above is that the final column is called 'WindDirDegrees< br />'.\n", "\n", "When the dataset was exported from the Weather Underground web site html line breaks were automatically added to each line in the file which read_csv() has interpreted as part of the column name and its values. This can be seen more clearly by looking at more values in the final column:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 186
\n", "1 214
\n", "2 219
\n", "3 211
\n", "4 199
\n", "Name: WindDirDegrees
, dtype: object" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "london['WindDirDegrees
'].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "'WindDirDegrees< br />' can be changed to 'WindDirDegrees' with the rename() method as follows:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "london = london.rename(columns={'WindDirDegrees
' : 'WindDirDegrees'})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To remove the '< br />' html line breaks from the values in the 'WindDirDegrees' column you need to use the string method rstrip() which is used to remove characters from the *end* or 'rear' of a string:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "london['WindDirDegrees'] = london['WindDirDegrees'].str.rstrip('
')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Display the first few rows of the 'WindDirDegrees' to confirm the change:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 186\n", "1 214\n", "2 219\n", "3 211\n", "4 199\n", "Name: WindDirDegrees, dtype: object" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "london['WindDirDegrees'].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Missing values\n", "\n", "Missing (also called null or not available) values are marked as NaN (not a number) in dataframes." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "360 Rain\n", "361 NaN\n", "362 NaN\n", "363 NaN\n", "364 Fog\n", "Name: Events, dtype: object" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "london['Events'].tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `isnull()` method returns `True` for each row in a column that has a null value. The method can be used to select and display those rows. Scroll the table below to the right to check that the events column is only showing missing values." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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GMTMax TemperatureCMean TemperatureCMin TemperatureCDew PointCMeanDew PointCMin DewpointCMax HumidityMean HumidityMin Humidity...Max VisibilityKmMean VisibilityKmMin VisibilitykMMax Wind SpeedKm/hMean Wind SpeedKm/hMax Gust SpeedKm/hPrecipitationmmCloudCoverEventsWindDirDegrees
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332014-2-3863532937955...3115103218NaN0.004.0NaN133
432014-2-1386321-1816645...311510422748.00.002.0NaN236
522014-2-221284531876838...3116102921NaN0.002.0NaN228
632014-3-5137042-11007240...31158216NaN0.251.0NaN248
642014-3-61395753937750...31157321442.00.002.0NaN206
662014-3-816117742936529...311162718NaN0.006.0NaN156
672014-3-919126743815828...2695241034.00.00NaNNaN170
682014-3-101384842877357...31126238NaN0.006.0NaN27
702014-3-121692841937640...1993118NaN0.004.0NaN31
732014-3-1518127652876424...311852716NaN0.001.0NaN277
742014-3-1619137863876328...311972416NaN0.00NaNNaN266
752014-3-171495764937447...311352114NaN0.003.0NaN263
772014-3-1916117754936835...311882618NaN0.002.0NaN241
872014-3-291913874-1935718...31933416NaN0.00NaNNaN99
922014-4-31814111199887552...21732611NaN0.005.0NaN96
932014-4-4151291086887146...311122414NaN0.005.0NaN253
982014-4-917104763936838...311462313NaN0.002.0NaN261
992014-4-1016116653936231...311491910NaN0.003.0NaN278
1002014-4-1116129542715328...311810138NaN0.001.0NaN351
1012014-4-1214106753876642...3116102911NaN0.001.0NaN262
1022014-4-1316128852936333...311492614NaN0.002.0NaN283
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" ], "text/plain": [ " GMT Max TemperatureC Mean TemperatureC Min TemperatureC \\\n", "32 2014-2-2 10 8 5 \n", "33 2014-2-3 8 6 3 \n", "43 2014-2-13 8 6 3 \n", "52 2014-2-22 12 8 4 \n", "63 2014-3-5 13 7 0 \n", "64 2014-3-6 13 9 5 \n", "66 2014-3-8 16 11 7 \n", "67 2014-3-9 19 12 6 \n", "68 2014-3-10 13 8 4 \n", "70 2014-3-12 16 9 2 \n", "73 2014-3-15 18 12 7 \n", "74 2014-3-16 19 13 7 \n", "75 2014-3-17 14 9 5 \n", "77 2014-3-19 16 11 7 \n", "87 2014-3-29 19 13 8 \n", "92 2014-4-3 18 14 11 \n", "93 2014-4-4 15 12 9 \n", "98 2014-4-9 17 10 4 \n", "99 2014-4-10 16 11 6 \n", "100 2014-4-11 16 12 9 \n", "101 2014-4-12 14 10 6 \n", "102 2014-4-13 16 12 8 \n", "103 2014-4-14 16 11 6 \n", "104 2014-4-15 14 9 3 \n", "105 2014-4-16 17 10 3 \n", "106 2014-4-17 17 11 6 \n", "107 2014-4-18 12 9 6 \n", "108 2014-4-19 14 9 3 \n", "122 2014-5-3 14 8 2 \n", "123 2014-5-4 16 10 4 \n", ".. ... ... ... ... \n", "252 2014-9-10 21 16 11 \n", "253 2014-9-11 18 14 10 \n", "254 2014-9-12 23 18 13 \n", "255 2014-9-13 21 17 12 \n", "257 2014-9-15 22 17 12 \n", "258 2014-9-16 23 18 13 \n", "259 2014-9-17 21 18 16 \n", "260 2014-9-18 26 20 14 \n", "264 2014-9-22 19 13 7 \n", "267 2014-9-25 19 13 7 \n", "269 2014-9-27 21 17 12 \n", "270 2014-9-28 24 19 14 \n", "272 2014-9-30 22 18 14 \n", "274 2014-10-2 22 19 14 \n", "292 2014-10-20 17 14 12 \n", "295 2014-10-23 16 13 11 \n", "297 2014-10-25 16 11 7 \n", "298 2014-10-26 16 13 12 \n", "299 2014-10-27 18 16 12 \n", "302 2014-10-30 18 16 13 \n", "303 2014-10-31 23 17 12 \n", "322 2014-11-19 11 9 7 \n", "323 2014-11-20 11 8 6 \n", "339 2014-12-6 6 2 -1 \n", "346 2014-12-13 7 3 0 \n", "351 2014-12-18 13 12 11 \n", "358 2014-12-25 8 6 2 \n", "361 2014-12-28 6 2 -2 \n", "362 2014-12-29 6 1 -4 \n", "363 2014-12-30 6 1 -4 \n", "\n", " Dew PointC MeanDew PointC Min DewpointC Max Humidity Mean Humidity \\\n", "32 4 3 1 93 71 \n", "33 5 3 2 93 79 \n", "43 2 1 -1 81 66 \n", "52 5 3 1 87 68 \n", "63 4 2 -1 100 72 \n", "64 7 5 3 93 77 \n", "66 7 4 2 93 65 \n", "67 7 4 3 81 58 \n", "68 8 4 2 87 73 \n", "70 8 4 1 93 76 \n", "73 6 5 2 87 64 \n", "74 8 6 3 87 63 \n", "75 7 6 4 93 74 \n", "77 7 5 4 93 68 \n", "87 7 4 -1 93 57 \n", "92 11 9 9 88 75 \n", "93 10 8 6 88 71 \n", "98 7 6 3 93 68 \n", "99 6 5 3 93 62 \n", "100 5 4 2 71 53 \n", "101 7 5 3 87 66 \n", "102 8 5 2 93 63 \n", "103 5 3 0 87 57 \n", "104 4 2 0 81 58 \n", "105 4 1 -6 87 54 \n", "106 7 4 1 81 59 \n", "107 5 2 -1 81 57 \n", "108 4 2 0 81 59 \n", "122 3 1 -1 87 56 \n", "123 6 4 2 87 62 \n", ".. ... ... ... ... ... \n", "252 12 10 8 94 68 \n", "253 11 10 8 94 70 \n", "254 14 12 9 94 70 \n", "255 14 12 11 100 75 \n", "257 14 12 9 100 74 \n", "258 16 14 11 100 77 \n", "259 15 14 13 94 78 \n", "260 16 15 13 94 74 \n", "264 9 8 6 100 68 \n", "267 12 9 5 93 71 \n", "269 14 12 11 94 71 \n", "270 15 14 13 94 72 \n", "272 15 13 12 94 78 \n", "274 14 13 11 94 75 \n", "292 11 11 9 88 76 \n", "295 14 10 7 94 76 \n", "297 9 8 6 100 75 \n", "298 9 8 7 77 67 \n", "299 13 12 9 94 80 \n", "302 15 14 12 100 89 \n", "303 15 13 11 100 78 \n", "322 9 7 5 100 88 \n", "323 8 6 5 93 87 \n", "339 3 1 -1 100 85 \n", "346 2 1 -1 100 83 \n", "351 11 11 10 94 85 \n", "358 3 2 1 93 79 \n", "361 1 -2 -3 100 76 \n", "362 2 -1 -4 100 87 \n", "363 2 -1 -4 100 90 \n", "\n", " Min Humidity ... Max VisibilityKm Mean VisibilityKm \\\n", "32 43 ... 31 16 \n", "33 55 ... 31 15 \n", "43 45 ... 31 15 \n", "52 38 ... 31 16 \n", "63 40 ... 31 15 \n", "64 50 ... 31 15 \n", "66 29 ... 31 11 \n", "67 28 ... 26 9 \n", "68 57 ... 31 12 \n", "70 40 ... 19 9 \n", "73 24 ... 31 18 \n", "74 28 ... 31 19 \n", "75 47 ... 31 13 \n", "77 35 ... 31 18 \n", "87 18 ... 31 9 \n", "92 52 ... 21 7 \n", "93 46 ... 31 11 \n", "98 38 ... 31 14 \n", "99 31 ... 31 14 \n", "100 28 ... 31 18 \n", "101 42 ... 31 16 \n", "102 33 ... 31 14 \n", "103 25 ... 31 17 \n", "104 26 ... 31 20 \n", "105 15 ... 31 20 \n", "106 27 ... 31 17 \n", "107 28 ... 31 15 \n", "108 33 ... 31 14 \n", "122 26 ... 31 16 \n", "123 32 ... 31 17 \n", ".. ... ... ... ... \n", "252 38 ... 23 13 \n", "253 45 ... 13 8 \n", "254 33 ... 31 11 \n", "255 45 ... 31 12 \n", "257 35 ... 26 12 \n", "258 39 ... 19 6 \n", "259 59 ... 11 6 \n", "260 43 ... 19 6 \n", "264 35 ... 19 10 \n", "267 48 ... 31 17 \n", "269 43 ... 31 15 \n", "270 41 ... 27 13 \n", "272 44 ... 27 10 \n", "274 46 ... 31 15 \n", "292 50 ... 31 15 \n", "295 58 ... 31 14 \n", "297 41 ... 31 13 \n", "298 45 ... 31 12 \n", "299 55 ... 31 14 \n", "302 72 ... 13 6 \n", "303 40 ... 31 13 \n", "322 70 ... 10 5 \n", "323 66 ... 13 6 \n", "339 60 ... 31 15 \n", "346 53 ... 31 19 \n", "351 74 ... 31 12 \n", "358 51 ... 31 19 \n", "361 45 ... 31 17 \n", "362 62 ... 24 10 \n", "363 68 ... 27 9 \n", "\n", " Min VisibilitykM Max Wind SpeedKm/h Mean Wind SpeedKm/h \\\n", "32 10 37 24 \n", "33 10 32 18 \n", "43 10 42 27 \n", "52 10 29 21 \n", "63 8 21 6 \n", "64 7 32 14 \n", "66 6 27 18 \n", "67 5 24 10 \n", "68 6 23 8 \n", "70 3 11 8 \n", "73 5 27 16 \n", "74 7 24 16 \n", "75 5 21 14 \n", "77 8 26 18 \n", "87 3 34 16 \n", "92 3 26 11 \n", "93 2 24 14 \n", "98 6 23 13 \n", "99 9 19 10 \n", "100 10 13 8 \n", "101 10 29 11 \n", "102 9 26 14 \n", "103 10 21 13 \n", "104 10 21 10 \n", "105 10 16 11 \n", "106 10 21 11 \n", "107 10 19 13 \n", "108 10 23 11 \n", "122 10 14 8 \n", "123 10 19 10 \n", ".. ... ... ... \n", "252 9 21 8 \n", "253 5 11 6 \n", "254 5 19 11 \n", "255 3 19 11 \n", "257 5 14 6 \n", "258 2 19 8 \n", "259 3 19 13 \n", "260 2 19 11 \n", "264 7 11 6 \n", "267 10 26 13 \n", "269 10 13 5 \n", "270 6 16 6 \n", "272 4 19 13 \n", "274 6 21 8 \n", "292 10 27 19 \n", "295 9 23 16 \n", "297 10 26 13 \n", "298 10 27 18 \n", "299 8 29 16 \n", "302 1 16 10 \n", "303 3 23 8 \n", "322 3 14 10 \n", "323 3 16 8 \n", "339 10 14 8 \n", "346 9 13 8 \n", "351 10 37 29 \n", "358 10 24 16 \n", "361 8 16 11 \n", "362 5 13 8 \n", "363 3 14 6 \n", "\n", " Max Gust SpeedKm/h Precipitationmm CloudCover Events WindDirDegrees \n", "32 47.0 0.00 2.0 NaN 216 \n", "33 NaN 0.00 4.0 NaN 133 \n", "43 48.0 0.00 2.0 NaN 236 \n", "52 NaN 0.00 2.0 NaN 228 \n", "63 NaN 0.25 1.0 NaN 248 \n", "64 42.0 0.00 2.0 NaN 206 \n", "66 NaN 0.00 6.0 NaN 156 \n", "67 34.0 0.00 NaN NaN 170 \n", "68 NaN 0.00 6.0 NaN 27 \n", "70 NaN 0.00 4.0 NaN 31 \n", "73 NaN 0.00 1.0 NaN 277 \n", "74 NaN 0.00 NaN NaN 266 \n", "75 NaN 0.00 3.0 NaN 263 \n", "77 NaN 0.00 2.0 NaN 241 \n", "87 NaN 0.00 NaN NaN 99 \n", "92 NaN 0.00 5.0 NaN 96 \n", "93 NaN 0.00 5.0 NaN 253 \n", "98 NaN 0.00 2.0 NaN 261 \n", "99 NaN 0.00 3.0 NaN 278 \n", "100 NaN 0.00 1.0 NaN 351 \n", "101 NaN 0.00 1.0 NaN 262 \n", "102 NaN 0.00 2.0 NaN 283 \n", "103 NaN 0.00 1.0 NaN 330 \n", "104 NaN 0.00 1.0 NaN 64 \n", "105 NaN 0.00 1.0 NaN 139 \n", "106 NaN 0.00 2.0 NaN 279 \n", "107 NaN 0.00 3.0 NaN 21 \n", "108 NaN 0.00 4.0 NaN 56 \n", "122 NaN 0.00 1.0 NaN 39 \n", "123 40.0 0.00 1.0 NaN 196 \n", ".. ... ... ... ... ... \n", "252 NaN 0.00 2.0 NaN 92 \n", "253 NaN 0.00 6.0 NaN 42 \n", "254 NaN 0.00 3.0 NaN 76 \n", "255 NaN 0.00 4.0 NaN 56 \n", "257 NaN 0.00 3.0 NaN 71 \n", "258 NaN 0.00 5.0 NaN 70 \n", "259 NaN 0.00 5.0 NaN 58 \n", "260 35.0 0.00 5.0 NaN 71 \n", "264 NaN 0.25 1.0 NaN 309 \n", "267 NaN 0.00 5.0 NaN 249 \n", "269 NaN 0.00 6.0 NaN 146 \n", "270 NaN 0.00 4.0 NaN 116 \n", "272 32.0 0.00 4.0 NaN 222 \n", "274 35.0 0.00 3.0 NaN 203 \n", "292 NaN 0.25 3.0 NaN 238 \n", "295 NaN 0.00 5.0 NaN 214 \n", "297 NaN 0.00 4.0 NaN 237 \n", "298 40.0 0.00 5.0 NaN 217 \n", "299 40.0 0.00 3.0 NaN 192 \n", "302 NaN 0.00 5.0 NaN 170 \n", "303 48.0 0.00 1.0 NaN 170 \n", "322 NaN 0.00 2.0 NaN 93 \n", "323 NaN 0.00 3.0 NaN 69 \n", "339 NaN 0.25 4.0 NaN 265 \n", "346 NaN 0.00 2.0 NaN 278 \n", "351 60.0 0.00 6.0 NaN 245 \n", "358 NaN 0.00 1.0 NaN 275 \n", "361 NaN 0.00 1.0 NaN 5 \n", "362 NaN 0.25 1.0 NaN 289 \n", "363 NaN 0.25 3.0 NaN 258 \n", "\n", "[114 rows x 23 columns]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "london[london['Events'].isnull()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One way to deal with missing values is to replace them by some value. The column method `fillna()` fills all not available value cells with the value given as argument. In the example below, each missing event is replaced by the empty string." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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GMTMax TemperatureCMean TemperatureCMin TemperatureCDew PointCMeanDew PointCMin DewpointCMax HumidityMean HumidityMin Humidity...Max VisibilityKmMean VisibilityKmMin VisibilitykMMax Wind SpeedKm/hMean Wind SpeedKm/hMax Gust SpeedKm/hPrecipitationmmCloudCoverEventsWindDirDegrees
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0 rows × 23 columns

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" ], "text/plain": [ "Empty DataFrame\n", "Columns: [GMT, Max TemperatureC, Mean TemperatureC, Min TemperatureC, Dew PointC, MeanDew PointC, Min DewpointC, Max Humidity, Mean Humidity, Min Humidity, Max Sea Level PressurehPa, Mean Sea Level PressurehPa, Min Sea Level PressurehPa, Max VisibilityKm, Mean VisibilityKm, Min VisibilitykM, Max Wind SpeedKm/h, Mean Wind SpeedKm/h, Max Gust SpeedKm/h, Precipitationmm, CloudCover, Events, WindDirDegrees]\n", "Index: []\n", "\n", "[0 rows x 23 columns]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "london['Events'] = london['Events'].fillna('')\n", "london[london['Events'].isnull()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The empty dataframe (no rows) confirms there are no more missing event values.\n", "\n", "Another way to deal with missing values is to ignore rows with them. The `dropna()` dataframe method returns a new dataframe where all rows with at least one non-available value have been removed." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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GMTMax TemperatureCMean TemperatureCMin TemperatureCDew PointCMeanDew PointCMin DewpointCMax HumidityMean HumidityMin Humidity...Max VisibilityKmMean VisibilityKmMin VisibilitykMMax Wind SpeedKm/hMean Wind SpeedKm/hMax Gust SpeedKm/hPrecipitationmmCloudCoverEventsWindDirDegrees
02014-1-11186974948673...31112402666.09.914.0Rain186
12014-1-21196964948160...31155322150.01.022.0Rain214
22014-1-31186952947654...31120502969.07.112.0Rain-Thunderstorm219
32014-1-4963862938565...31125352139.09.914.0Rain211
52014-1-6121081186947860...31125502961.02.034.0Rain219
62014-1-712108876877863...31135392755.05.083.0Rain219
72014-1-812971176948569...27146241837.01.023.0Rain197
82014-1-911851152947653...31135402660.00.004.0Rain247
92014-1-101172742938566...311710241332.00.253.0Rain216
152014-1-161186964948567...31143271847.03.053.0Rain188
162014-1-17975764938668...31135291947.04.063.0Rain192
212014-1-2211867641008762...31136231432.00.255.0Rain221
222014-1-238626301008150...31145261345.01.022.0Rain265
242014-1-2512958601008348...31102401361.02.034.0Fog-Rain249
252014-1-26962830938062...31143371939.05.084.0Rain232
262014-1-2785242-1878061...31169342347.02.033.0Rain228
302014-1-319518411009072...2370371350.07.115.0Fog-Rain173
312014-2-19759301007346...31133502471.00.002.0Rain220
322014-2-21085431937143...311610372447.00.002.0216
342014-2-4974431877650...31146392153.00.514.0Rain176
352014-2-51085742937556...26136453466.02.034.0Rain192
362014-2-610869631008262...29122352640.07.114.0Rain190
372014-2-79749401007846...31123391958.00.514.0Rain230
382014-2-8987840937450...31143453269.02.033.0Rain208
392014-2-987431-2816542...311410473466.00.003.0Rain228
412014-2-1185262-1937543...31146401958.02.033.0Rain222
422014-2-1296262-1937449...31145522676.07.873.0Rain-Thunderstorm211
432014-2-1386321-1816645...311510422748.00.002.0236
442014-2-1412831040948160...31145632490.08.895.0Rain180
452014-2-151185532876751...31159664785.00.514.0Rain237
..................................................................
2812014-10-918141111108887748...31153392153.02.032.0Rain-Thunderstorm209
2822014-10-101813912108948045...311710261442.03.052.0Rain207
2842014-10-121410611851008457...3114623637.02.032.0Fog-Rain53
2882014-10-161814111412111008758...31126211034.00.252.0Rain214
2892014-10-171916131514121008462...29148241342.00.512.0Rain188
2912014-10-19211714161311947848...31145342147.00.003.0Rain225
2932014-10-21141181361946537...31143472769.03.053.0Rain268
2982014-10-26161312987776745...311210271840.00.005.0217
2992014-10-2718161213129948055...31148291640.00.003.0192
3002014-10-2818149141291008347...31130241137.00.003.0Fog-Rain181
3032014-10-312317121513111007840...3113323848.00.001.0170
3042014-11-118161315116947439...29139321848.00.253.0Rain211
3052014-11-216131013118948264...29140272350.011.944.0Rain207
3062014-11-31086974948460...31142231434.05.083.0Rain208
3102014-11-7131081285958052...31112422342.05.084.0Rain207
3112014-11-8141171185948464...31145341852.02.033.0Rain192
3122014-11-9131069741008347...3111014632.00.002.0Fog212
3362014-12-3864531937856...31159231637.00.514.0Rain17
3422014-12-9126-1113-11008664...31153371452.00.255.0Rain224
3452014-12-1296294-1947959...31113402655.01.023.0Rain253
3472014-12-14104-193-11008462...31169291445.00.253.0Rain215
3502014-12-171394111061009076...31126342155.00.255.0Rain252
3512014-12-18131211111110948574...311210372960.00.006.0245
3532014-12-20974432877553...311510321945.00.003.0Rain251
3542014-12-211283963938065...311210352347.00.006.0Rain237
3552014-12-221312101197947763...31148403255.00.005.0Rain240
3562014-12-23131110975827258...311210423261.00.004.0Rain238
3572014-12-241284104-2947138...311310342645.00.255.0Rain262
3602014-12-2743281-1938364...31139422661.00.004.0Rain330
3642014-12-3182-452-41008970...199023837.00.254.0Fog201
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128 rows × 23 columns

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" ], "text/plain": [ " GMT Max TemperatureC Mean TemperatureC Min TemperatureC \\\n", "0 2014-1-1 11 8 6 \n", "1 2014-1-2 11 9 6 \n", "2 2014-1-3 11 8 6 \n", "3 2014-1-4 9 6 3 \n", "5 2014-1-6 12 10 8 \n", "6 2014-1-7 12 10 8 \n", "7 2014-1-8 12 9 7 \n", "8 2014-1-9 11 8 5 \n", "9 2014-1-10 11 7 2 \n", "15 2014-1-16 11 8 6 \n", "16 2014-1-17 9 7 5 \n", "21 2014-1-22 11 8 6 \n", "22 2014-1-23 8 6 2 \n", "24 2014-1-25 12 9 5 \n", "25 2014-1-26 9 6 2 \n", "26 2014-1-27 8 5 2 \n", "30 2014-1-31 9 5 1 \n", "31 2014-2-1 9 7 5 \n", "32 2014-2-2 10 8 5 \n", "34 2014-2-4 9 7 4 \n", "35 2014-2-5 10 8 5 \n", "36 2014-2-6 10 8 6 \n", "37 2014-2-7 9 7 4 \n", "38 2014-2-8 9 8 7 \n", "39 2014-2-9 8 7 4 \n", "41 2014-2-11 8 5 2 \n", "42 2014-2-12 9 6 2 \n", "43 2014-2-13 8 6 3 \n", "44 2014-2-14 12 8 3 \n", "45 2014-2-15 11 8 5 \n", ".. ... ... ... ... \n", "281 2014-10-9 18 14 11 \n", "282 2014-10-10 18 13 9 \n", "284 2014-10-12 14 10 6 \n", "288 2014-10-16 18 14 11 \n", "289 2014-10-17 19 16 13 \n", "291 2014-10-19 21 17 14 \n", "293 2014-10-21 14 11 8 \n", "298 2014-10-26 16 13 12 \n", "299 2014-10-27 18 16 12 \n", "300 2014-10-28 18 14 9 \n", "303 2014-10-31 23 17 12 \n", "304 2014-11-1 18 16 13 \n", "305 2014-11-2 16 13 10 \n", "306 2014-11-3 10 8 6 \n", "310 2014-11-7 13 10 8 \n", "311 2014-11-8 14 11 7 \n", "312 2014-11-9 13 10 6 \n", "336 2014-12-3 8 6 4 \n", "342 2014-12-9 12 6 -1 \n", "345 2014-12-12 9 6 2 \n", "347 2014-12-14 10 4 -1 \n", "350 2014-12-17 13 9 4 \n", "351 2014-12-18 13 12 11 \n", "353 2014-12-20 9 7 4 \n", "354 2014-12-21 12 8 3 \n", "355 2014-12-22 13 12 10 \n", "356 2014-12-23 13 11 10 \n", "357 2014-12-24 12 8 4 \n", "360 2014-12-27 4 3 2 \n", "364 2014-12-31 8 2 -4 \n", "\n", " Dew PointC MeanDew PointC Min DewpointC Max Humidity Mean Humidity \\\n", "0 9 7 4 94 86 \n", "1 9 6 4 94 81 \n", "2 9 5 2 94 76 \n", "3 8 6 2 93 85 \n", "5 11 8 6 94 78 \n", "6 8 7 6 87 78 \n", "7 11 7 6 94 85 \n", "8 11 5 2 94 76 \n", "9 7 4 2 93 85 \n", "15 9 6 4 94 85 \n", "16 7 6 4 93 86 \n", "21 7 6 4 100 87 \n", "22 6 3 0 100 81 \n", "24 8 6 0 100 83 \n", "25 8 3 0 93 80 \n", "26 4 2 -1 87 80 \n", "30 8 4 1 100 90 \n", "31 9 3 0 100 73 \n", "32 4 3 1 93 71 \n", "34 4 3 1 87 76 \n", "35 7 4 2 93 75 \n", "36 9 6 3 100 82 \n", "37 9 4 0 100 78 \n", "38 8 4 0 93 74 \n", "39 3 1 -2 81 65 \n", "41 6 2 -1 93 75 \n", "42 6 2 -1 93 74 \n", "43 2 1 -1 81 66 \n", "44 10 4 0 94 81 \n", "45 5 3 2 87 67 \n", ".. ... ... ... ... ... \n", "281 11 10 8 88 77 \n", "282 12 10 8 94 80 \n", "284 11 8 5 100 84 \n", "288 14 12 11 100 87 \n", "289 15 14 12 100 84 \n", "291 16 13 11 94 78 \n", "293 13 6 1 94 65 \n", "298 9 8 7 77 67 \n", "299 13 12 9 94 80 \n", "300 14 12 9 100 83 \n", "303 15 13 11 100 78 \n", "304 15 11 6 94 74 \n", "305 13 11 8 94 82 \n", "306 9 7 4 94 84 \n", "310 12 8 5 95 80 \n", "311 11 8 5 94 84 \n", "312 9 7 4 100 83 \n", "336 5 3 1 93 78 \n", "342 11 3 -1 100 86 \n", "345 9 4 -1 94 79 \n", "347 9 3 -1 100 84 \n", "350 11 10 6 100 90 \n", "351 11 11 10 94 85 \n", "353 4 3 2 87 75 \n", "354 9 6 3 93 80 \n", "355 11 9 7 94 77 \n", "356 9 7 5 82 72 \n", "357 10 4 -2 94 71 \n", "360 8 1 -1 93 83 \n", "364 5 2 -4 100 89 \n", "\n", " Min Humidity ... Max VisibilityKm Mean VisibilityKm \\\n", "0 73 ... 31 11 \n", "1 60 ... 31 15 \n", "2 54 ... 31 12 \n", "3 65 ... 31 12 \n", "5 60 ... 31 12 \n", "6 63 ... 31 13 \n", "7 69 ... 27 14 \n", "8 53 ... 31 13 \n", "9 66 ... 31 17 \n", "15 67 ... 31 14 \n", "16 68 ... 31 13 \n", "21 62 ... 31 13 \n", "22 50 ... 31 14 \n", "24 48 ... 31 10 \n", "25 62 ... 31 14 \n", "26 61 ... 31 16 \n", "30 72 ... 23 7 \n", "31 46 ... 31 13 \n", "32 43 ... 31 16 \n", "34 50 ... 31 14 \n", "35 56 ... 26 13 \n", "36 62 ... 29 12 \n", "37 46 ... 31 12 \n", "38 50 ... 31 14 \n", "39 42 ... 31 14 \n", "41 43 ... 31 14 \n", "42 49 ... 31 14 \n", "43 45 ... 31 15 \n", "44 60 ... 31 14 \n", "45 51 ... 31 15 \n", ".. ... ... ... ... \n", "281 48 ... 31 15 \n", "282 45 ... 31 17 \n", "284 57 ... 31 14 \n", "288 58 ... 31 12 \n", "289 62 ... 29 14 \n", "291 48 ... 31 14 \n", "293 37 ... 31 14 \n", "298 45 ... 31 12 \n", "299 55 ... 31 14 \n", "300 47 ... 31 13 \n", "303 40 ... 31 13 \n", "304 39 ... 29 13 \n", "305 64 ... 29 14 \n", "306 60 ... 31 14 \n", "310 52 ... 31 11 \n", "311 64 ... 31 14 \n", "312 47 ... 31 11 \n", "336 56 ... 31 15 \n", "342 64 ... 31 15 \n", "345 59 ... 31 11 \n", "347 62 ... 31 16 \n", "350 76 ... 31 12 \n", "351 74 ... 31 12 \n", "353 53 ... 31 15 \n", "354 65 ... 31 12 \n", "355 63 ... 31 14 \n", "356 58 ... 31 12 \n", "357 38 ... 31 13 \n", "360 64 ... 31 13 \n", "364 70 ... 19 9 \n", "\n", " Min VisibilitykM Max Wind SpeedKm/h Mean Wind SpeedKm/h \\\n", "0 2 40 26 \n", "1 5 32 21 \n", "2 0 50 29 \n", "3 5 35 21 \n", "5 5 50 29 \n", "6 5 39 27 \n", "7 6 24 18 \n", "8 5 40 26 \n", "9 10 24 13 \n", "15 3 27 18 \n", "16 5 29 19 \n", "21 6 23 14 \n", "22 5 26 13 \n", "24 2 40 13 \n", "25 3 37 19 \n", "26 9 34 23 \n", "30 0 37 13 \n", "31 3 50 24 \n", "32 10 37 24 \n", "34 6 39 21 \n", "35 6 45 34 \n", "36 2 35 26 \n", "37 3 39 19 \n", "38 3 45 32 \n", "39 10 47 34 \n", "41 6 40 19 \n", "42 5 52 26 \n", "43 10 42 27 \n", "44 5 63 24 \n", "45 9 66 47 \n", ".. ... ... ... \n", "281 3 39 21 \n", "282 10 26 14 \n", "284 6 23 6 \n", "288 6 21 10 \n", "289 8 24 13 \n", "291 5 34 21 \n", "293 3 47 27 \n", "298 10 27 18 \n", "299 8 29 16 \n", "300 0 24 11 \n", "303 3 23 8 \n", "304 9 32 18 \n", "305 0 27 23 \n", "306 2 23 14 \n", "310 2 42 23 \n", "311 5 34 18 \n", "312 0 14 6 \n", "336 9 23 16 \n", "342 3 37 14 \n", "345 3 40 26 \n", "347 9 29 14 \n", "350 6 34 21 \n", "351 10 37 29 \n", "353 10 32 19 \n", "354 10 35 23 \n", "355 8 40 32 \n", "356 10 42 32 \n", "357 10 34 26 \n", "360 9 42 26 \n", "364 0 23 8 \n", "\n", " Max Gust SpeedKm/h Precipitationmm CloudCover Events \\\n", "0 66.0 9.91 4.0 Rain \n", "1 50.0 1.02 2.0 Rain \n", "2 69.0 7.11 2.0 Rain-Thunderstorm \n", "3 39.0 9.91 4.0 Rain \n", "5 61.0 2.03 4.0 Rain \n", "6 55.0 5.08 3.0 Rain \n", "7 37.0 1.02 3.0 Rain \n", "8 60.0 0.00 4.0 Rain \n", "9 32.0 0.25 3.0 Rain \n", "15 47.0 3.05 3.0 Rain \n", "16 47.0 4.06 3.0 Rain \n", "21 32.0 0.25 5.0 Rain \n", "22 45.0 1.02 2.0 Rain \n", "24 61.0 2.03 4.0 Fog-Rain \n", "25 39.0 5.08 4.0 Rain \n", "26 47.0 2.03 3.0 Rain \n", "30 50.0 7.11 5.0 Fog-Rain \n", "31 71.0 0.00 2.0 Rain \n", "32 47.0 0.00 2.0 \n", "34 53.0 0.51 4.0 Rain \n", "35 66.0 2.03 4.0 Rain \n", "36 40.0 7.11 4.0 Rain \n", "37 58.0 0.51 4.0 Rain \n", "38 69.0 2.03 3.0 Rain \n", "39 66.0 0.00 3.0 Rain \n", "41 58.0 2.03 3.0 Rain \n", "42 76.0 7.87 3.0 Rain-Thunderstorm \n", "43 48.0 0.00 2.0 \n", "44 90.0 8.89 5.0 Rain \n", "45 85.0 0.51 4.0 Rain \n", ".. ... ... ... ... \n", "281 53.0 2.03 2.0 Rain-Thunderstorm \n", "282 42.0 3.05 2.0 Rain \n", "284 37.0 2.03 2.0 Fog-Rain \n", "288 34.0 0.25 2.0 Rain \n", "289 42.0 0.51 2.0 Rain \n", "291 47.0 0.00 3.0 Rain \n", "293 69.0 3.05 3.0 Rain \n", "298 40.0 0.00 5.0 \n", "299 40.0 0.00 3.0 \n", "300 37.0 0.00 3.0 Fog-Rain \n", "303 48.0 0.00 1.0 \n", "304 48.0 0.25 3.0 Rain \n", "305 50.0 11.94 4.0 Rain \n", "306 34.0 5.08 3.0 Rain \n", "310 42.0 5.08 4.0 Rain \n", "311 52.0 2.03 3.0 Rain \n", "312 32.0 0.00 2.0 Fog \n", "336 37.0 0.51 4.0 Rain \n", "342 52.0 0.25 5.0 Rain \n", "345 55.0 1.02 3.0 Rain \n", "347 45.0 0.25 3.0 Rain \n", "350 55.0 0.25 5.0 Rain \n", "351 60.0 0.00 6.0 \n", "353 45.0 0.00 3.0 Rain \n", "354 47.0 0.00 6.0 Rain \n", "355 55.0 0.00 5.0 Rain \n", "356 61.0 0.00 4.0 Rain \n", "357 45.0 0.25 5.0 Rain \n", "360 61.0 0.00 4.0 Rain \n", "364 37.0 0.25 4.0 Fog \n", "\n", " WindDirDegrees \n", "0 186 \n", "1 214 \n", "2 219 \n", "3 211 \n", "5 219 \n", "6 219 \n", "7 197 \n", "8 247 \n", "9 216 \n", "15 188 \n", "16 192 \n", "21 221 \n", "22 265 \n", "24 249 \n", "25 232 \n", "26 228 \n", "30 173 \n", "31 220 \n", "32 216 \n", "34 176 \n", "35 192 \n", "36 190 \n", "37 230 \n", "38 208 \n", "39 228 \n", "41 222 \n", "42 211 \n", "43 236 \n", "44 180 \n", "45 237 \n", ".. ... \n", "281 209 \n", "282 207 \n", "284 53 \n", "288 214 \n", "289 188 \n", "291 225 \n", "293 268 \n", "298 217 \n", "299 192 \n", "300 181 \n", "303 170 \n", "304 211 \n", "305 207 \n", "306 208 \n", "310 207 \n", "311 192 \n", "312 212 \n", "336 17 \n", "342 224 \n", "345 253 \n", "347 215 \n", "350 252 \n", "351 245 \n", "353 251 \n", "354 237 \n", "355 240 \n", "356 238 \n", "357 262 \n", "360 330 \n", "364 201 \n", "\n", "[128 rows x 23 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "london.dropna()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the table above has fewer than 251 of the original 365 rows, so there must be further null values besides the 114 missing events." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Changing the value type of a column" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The type of every column in a dataframe can be determined by looking at the dataframe's `dtypes` attribute, like this:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "GMT object\n", "Max TemperatureC int64\n", "Mean TemperatureC int64\n", "Min TemperatureC int64\n", "Dew PointC int64\n", "MeanDew PointC int64\n", "Min DewpointC int64\n", "Max Humidity int64\n", "Mean Humidity int64\n", "Min Humidity int64\n", "Max Sea Level PressurehPa int64\n", "Mean Sea Level PressurehPa int64\n", "Min Sea Level PressurehPa int64\n", "Max VisibilityKm int64\n", "Mean VisibilityKm int64\n", "Min VisibilitykM int64\n", "Max Wind SpeedKm/h int64\n", "Mean Wind SpeedKm/h int64\n", "Max Gust SpeedKm/h float64\n", "Precipitationmm float64\n", "CloudCover float64\n", "Events object\n", "WindDirDegrees object\n", "dtype: object" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "london.dtypes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The type of all the values in a column can be changed using the astype() method. The following code will change the values in the 'WindDirDegrees' column from strings (`object`) to integers (int64)." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "london['WindDirDegrees'] = london['WindDirDegrees'].astype('int64') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function `to_datetime()` is needed to change the values in the `'GMT'` column from strings (`object`) to dates (`datetime64`):" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "GMT datetime64[ns]\n", "Max TemperatureC int64\n", "Mean TemperatureC int64\n", "Min TemperatureC int64\n", "Dew PointC int64\n", "MeanDew PointC int64\n", "Min DewpointC int64\n", "Max Humidity int64\n", "Mean Humidity int64\n", "Min Humidity int64\n", "Max Sea Level PressurehPa int64\n", "Mean Sea Level PressurehPa int64\n", "Min Sea Level PressurehPa int64\n", "Max VisibilityKm int64\n", "Mean VisibilityKm int64\n", "Min VisibilitykM int64\n", "Max Wind SpeedKm/h int64\n", "Mean Wind SpeedKm/h int64\n", "Max Gust SpeedKm/h float64\n", "Precipitationmm float64\n", "CloudCover float64\n", "Events object\n", "WindDirDegrees int64\n", "dtype: object" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "london['GMT'] = to_datetime(london['GMT'])\n", "london.dtypes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Values of type `datetime64` can be created using the `datetime()` function where the first integer argument is the year, the second the month and the third the day. \n", "The code below will get and display the row in the dataframe whose `'GMT'` value is 4th June 2014." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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GMTMax TemperatureCMean TemperatureCMin TemperatureCDew PointCMeanDew PointCMin DewpointCMax HumidityMean HumidityMin Humidity...Max VisibilityKmMean VisibilityKmMin VisibilitykMMax Wind SpeedKm/hMean Wind SpeedKm/hMax Gust SpeedKm/hPrecipitationmmCloudCoverEventsWindDirDegrees
1542014-06-04141181193947847...31127471364.02.034.0Rain-Thunderstorm230
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1 rows × 23 columns

\n", "
" ], "text/plain": [ " GMT Max TemperatureC Mean TemperatureC Min TemperatureC \\\n", "154 2014-06-04 14 11 8 \n", "\n", " Dew PointC MeanDew PointC Min DewpointC Max Humidity Mean Humidity \\\n", "154 11 9 3 94 78 \n", "\n", " Min Humidity ... Max VisibilityKm Mean VisibilityKm \\\n", "154 47 ... 31 12 \n", "\n", " Min VisibilitykM Max Wind SpeedKm/h Mean Wind SpeedKm/h \\\n", "154 7 47 13 \n", "\n", " Max Gust SpeedKm/h Precipitationmm CloudCover Events \\\n", "154 64.0 2.03 4.0 Rain-Thunderstorm \n", "\n", " WindDirDegrees \n", "154 230 \n", "\n", "[1 rows x 23 columns]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "london[london['GMT'] == datetime(2014, 6, 4)] " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Queries such as 'Return all the rows where the date is between 8 December and 12 December' can be made:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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GMTMax TemperatureCMean TemperatureCMin TemperatureCDew PointCMeanDew PointCMin DewpointCMax HumidityMean HumidityMin Humidity...Max VisibilityKmMean VisibilityKmMin VisibilitykMMax Wind SpeedKm/hMean Wind SpeedKm/hMax Gust SpeedKm/hPrecipitationmmCloudCoverEventsWindDirDegrees
3412014-12-0874121-1937651...3117102313NaN0.002.0Rain270
3422014-12-09126-1113-11008664...31153371452.00.255.0Rain224
3432014-12-109761131907044...311834027NaN0.002.0Rain246
3442014-12-111085632827251...3115104031NaN0.252.0Rain246
3452014-12-1296294-1947959...31113402655.01.023.0Rain253
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5 rows × 23 columns

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" ], "text/plain": [ " GMT Max TemperatureC Mean TemperatureC Min TemperatureC \\\n", "341 2014-12-08 7 4 1 \n", "342 2014-12-09 12 6 -1 \n", "343 2014-12-10 9 7 6 \n", "344 2014-12-11 10 8 5 \n", "345 2014-12-12 9 6 2 \n", "\n", " Dew PointC MeanDew PointC Min DewpointC Max Humidity Mean Humidity \\\n", "341 2 1 -1 93 76 \n", "342 11 3 -1 100 86 \n", "343 11 3 1 90 70 \n", "344 6 3 2 82 72 \n", "345 9 4 -1 94 79 \n", "\n", " Min Humidity ... Max VisibilityKm Mean VisibilityKm \\\n", "341 51 ... 31 17 \n", "342 64 ... 31 15 \n", "343 44 ... 31 18 \n", "344 51 ... 31 15 \n", "345 59 ... 31 11 \n", "\n", " Min VisibilitykM Max Wind SpeedKm/h Mean Wind SpeedKm/h \\\n", "341 10 23 13 \n", "342 3 37 14 \n", "343 3 40 27 \n", "344 10 40 31 \n", "345 3 40 26 \n", "\n", " Max Gust SpeedKm/h Precipitationmm CloudCover Events WindDirDegrees \n", "341 NaN 0.00 2.0 Rain 270 \n", "342 52.0 0.25 5.0 Rain 224 \n", "343 NaN 0.00 2.0 Rain 246 \n", "344 NaN 0.25 2.0 Rain 246 \n", "345 55.0 1.02 3.0 Rain 253 \n", "\n", "[5 rows x 23 columns]" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dates = london['GMT']\n", "start = datetime(2014, 12, 8)\n", "end = datetime(2014, 12, 12)\n", "london[(dates >= start) & (dates <= end)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tasks\n", "\n", "Now that the wind direction is given by a number, write code to select all days that had a northerly wind. Hint: select the rows where the direction is greater than or equal to 350 **or** smaller than or equal to 10, as the compass rose shows." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the code cell below, write code to get and display all the rows in the dataframe that are beween 1 April 2014 and \n", "11 April 2014." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the cell below, write two lines of code to display the first five rows that have a missing value in the `'Max Gust SpeedKm/h'` column. Hint: first select the missing value rows and store them in a new dataframe, then display the first five rows of the new dataframe." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Now go back to the course step and mark it complete.**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 5: Every picture tells a story\n", "\n", "The following line of code tells Jupyter to display inside this notebook any graph that is created." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `plot()` method can make a graph of the values in a column. Gridlines are turned on by the `grid` argument." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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LYSkQojhfDJ0xtgrAewD8s/v/DMDbAXzXPeRBAO+rdR4alnJRrKZILrYsufidomMzZbSl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\nNjnv/t577xW/b3nxaSxbsRy53DU1rwUAdv+PAZi4dN16p77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FyalS9EKgtEp3XbgEG8b7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\ng6FLMArdYDAYuoT/Bza9D1WlurWgAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "london['Max Wind SpeedKm/h'].plot(grid=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The graph can be made bigger by giving the method a `figsize=(x,y)` argument where `x` and `y` are integers that determine the length of the x-axis and y-axis." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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D+hgqU67nK4N4fDffq0sbOLK8gRPnw0rRFwpV7N2WgWmwutIIsxkZTMXbyQz0IxBjxmZF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\neDBTcbw6o32nyMFa39EYD6by2RRcnys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/q3+tQe9ck+jXbzMFUu4OnU5bSvHstJbaA6+t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7/3A/h1AA9JknQUwEPOz5cFaPGrNx0Buu8ijQ2keCuKIIg+U+tJgO5taUEtVXRHCNzwPHRhYMaf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\nPs/v7rlmNPL7P3bnT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dZ6t7XAu2kybGnKNNoJ3OVPdawATtSWaWYzdfsGbiPzGWR6DfiT\nQ93AMmBv1fmlxveivAKMmdnzuUvhswLK9AqflSNpoaROb58P3EC212w30O/d6j1W814/sMt8l/Vs\noUSzb3JfcES2xyzvs8rG5ZypuzSHmZ2StB74iOzJvi1mNjrd952BLAJ2+J7COcCbZvahpH3AsKR7\ngJ+A2xPmmBxJbwGrgQWSjgCPA09TrNH7ZE90jAN/AndXnnBiSvRa7Y8PG9kTpPcBmNmopGHgEHAK\nWGdmp1PknZiVwJ3A174/A+AxwmdllOl1R/islC5gUFIb2aTGsJm9J+kQMCTpSWA/WZGKH1+XNE42\nIzWQIunElGm2S9JCsmW9A8D93r/ScRl/JxMEQRAEQdAE8QvoQRAEQRAETRDFVBAEQRAEQRNEMRUE\nQRAEQdAEUUwFQRAEQRA0QRRTQRAEQRAETRDFVBAEQRAEQRNEMRUEQRAEQdAE/wC6jmrX1R12agAA\nAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "london['Max Wind SpeedKm/h'].plot(grid=True, figsize=(10,5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Multiple lines can be plotted by selecting multiple columns." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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NjmIM8+mMQhIEkUXomRrcFNQzFdbMXXJFnwE99dQIPMQH2DxuMWE+SRSixpSg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\nWI4HQwFQngYakQ4fVjjNB6uKou6jyav5Mroq9vhXu5pvI4A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EXcxadX1v9LDbCNDlPWel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GcZI0YaFdJMrbQYUz+zpbq9YkpBTvO5ng/LDXrzRdN8FJ1W8gFxzZQMrAoZLbQ+RCtqi1k9phVK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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "london[['Max Wind SpeedKm/h', 'Mean Wind SpeedKm/h']].plot(grid=True, figsize=(10,5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Task\n", "\n", "In the cell below, write code to plot the minimum, mean, and maximum temperature during 2014 in London." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Now go back to the course step and mark it complete.**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise 6: Changing a dataframe's index\n", "Changing the dataframe's index from the default to `datetime64` values is done by assigning to the dataframe's `index` attribute the contents of the `'GMT`' column, like this:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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GMTMax TemperatureCMean TemperatureCMin TemperatureCDew PointCMeanDew PointCMin DewpointCMax HumidityMean HumidityMin Humidity...Max VisibilityKmMean VisibilityKmMin VisibilitykMMax Wind SpeedKm/hMean Wind SpeedKm/hMax Gust SpeedKm/hPrecipitationmmCloudCoverEventsWindDirDegrees
GMT
2014-01-012014-01-011186974948673...31112402666.09.914.0Rain186
2014-01-022014-01-021196964948160...31155322150.01.022.0Rain214
\n", "

2 rows × 23 columns

\n", "
" ], "text/plain": [ " GMT Max TemperatureC Mean TemperatureC Min TemperatureC \\\n", "GMT \n", "2014-01-01 2014-01-01 11 8 6 \n", "2014-01-02 2014-01-02 11 9 6 \n", "\n", " Dew PointC MeanDew PointC Min DewpointC Max Humidity \\\n", "GMT \n", "2014-01-01 9 7 4 94 \n", "2014-01-02 9 6 4 94 \n", "\n", " Mean Humidity Min Humidity ... Max VisibilityKm \\\n", "GMT ... \n", "2014-01-01 86 73 ... 31 \n", "2014-01-02 81 60 ... 31 \n", "\n", " Mean VisibilityKm Min VisibilitykM Max Wind SpeedKm/h \\\n", "GMT \n", "2014-01-01 11 2 40 \n", "2014-01-02 15 5 32 \n", "\n", " Mean Wind SpeedKm/h Max Gust SpeedKm/h Precipitationmm \\\n", "GMT \n", "2014-01-01 26 66.0 9.91 \n", "2014-01-02 21 50.0 1.02 \n", "\n", " CloudCover Events WindDirDegrees \n", "GMT \n", "2014-01-01 4.0 Rain 186 \n", "2014-01-02 2.0 Rain 214 \n", "\n", "[2 rows x 23 columns]" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "london.index = london['GMT']\n", "london.head(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `iloc` attribute can still be used to get and display rows by number, but now you can now also use the `datetime64` index to get a row by date, using the dataframe's `loc` attribute, like this:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "GMT 2014-01-01 00:00:00\n", "Max TemperatureC 11\n", "Mean TemperatureC 8\n", "Min TemperatureC 6\n", "Dew PointC 9\n", "MeanDew PointC 7\n", "Min DewpointC 4\n", "Max Humidity 94\n", "Mean Humidity 86\n", "Min Humidity 73\n", "Max Sea Level PressurehPa 1002\n", "Mean Sea Level PressurehPa 993\n", "Min Sea Level PressurehPa 984\n", "Max VisibilityKm 31\n", "Mean VisibilityKm 11\n", "Min VisibilitykM 2\n", "Max Wind SpeedKm/h 40\n", "Mean Wind SpeedKm/h 26\n", "Max Gust SpeedKm/h 66\n", "Precipitationmm 9.91\n", "CloudCover 4\n", "Events Rain\n", "WindDirDegrees 186\n", "Name: 2014-01-01 00:00:00, dtype: object" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "london.loc[datetime(2014, 1, 1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A query such as *'Return all the rows where the date is between December 8th and December 12th'* can now be done succinctly like this:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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GMTMax TemperatureCMean TemperatureCMin TemperatureCDew PointCMeanDew PointCMin DewpointCMax HumidityMean HumidityMin Humidity...Max VisibilityKmMean VisibilityKmMin VisibilitykMMax Wind SpeedKm/hMean Wind SpeedKm/hMax Gust SpeedKm/hPrecipitationmmCloudCoverEventsWindDirDegrees
GMT
2014-12-082014-12-0874121-1937651...3117102313NaN0.002.0Rain270
2014-12-092014-12-09126-1113-11008664...31153371452.00.255.0Rain224
2014-12-102014-12-109761131907044...311834027NaN0.002.0Rain246
2014-12-112014-12-111085632827251...3115104031NaN0.252.0Rain246
2014-12-122014-12-1296294-1947959...31113402655.01.023.0Rain253
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5 rows × 23 columns

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" ], "text/plain": [ " GMT Max TemperatureC Mean TemperatureC Min TemperatureC \\\n", "GMT \n", "2014-12-08 2014-12-08 7 4 1 \n", "2014-12-09 2014-12-09 12 6 -1 \n", "2014-12-10 2014-12-10 9 7 6 \n", "2014-12-11 2014-12-11 10 8 5 \n", "2014-12-12 2014-12-12 9 6 2 \n", "\n", " Dew PointC MeanDew PointC Min DewpointC Max Humidity \\\n", "GMT \n", "2014-12-08 2 1 -1 93 \n", "2014-12-09 11 3 -1 100 \n", "2014-12-10 11 3 1 90 \n", "2014-12-11 6 3 2 82 \n", "2014-12-12 9 4 -1 94 \n", "\n", " Mean Humidity Min Humidity ... Max VisibilityKm \\\n", "GMT ... \n", "2014-12-08 76 51 ... 31 \n", "2014-12-09 86 64 ... 31 \n", "2014-12-10 70 44 ... 31 \n", "2014-12-11 72 51 ... 31 \n", "2014-12-12 79 59 ... 31 \n", "\n", " Mean VisibilityKm Min VisibilitykM Max Wind SpeedKm/h \\\n", "GMT \n", "2014-12-08 17 10 23 \n", "2014-12-09 15 3 37 \n", "2014-12-10 18 3 40 \n", "2014-12-11 15 10 40 \n", "2014-12-12 11 3 40 \n", "\n", " Mean Wind SpeedKm/h Max Gust SpeedKm/h Precipitationmm \\\n", "GMT \n", "2014-12-08 13 NaN 0.00 \n", "2014-12-09 14 52.0 0.25 \n", "2014-12-10 27 NaN 0.00 \n", "2014-12-11 31 NaN 0.25 \n", "2014-12-12 26 55.0 1.02 \n", "\n", " CloudCover Events WindDirDegrees \n", "GMT \n", "2014-12-08 2.0 Rain 270 \n", "2014-12-09 5.0 Rain 224 \n", "2014-12-10 2.0 Rain 246 \n", "2014-12-11 2.0 Rain 246 \n", "2014-12-12 3.0 Rain 253 \n", "\n", "[5 rows x 23 columns]" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "london.loc[datetime(2014,12,8) : datetime(2014,12,12)]\n", "\n", "#The meaning of the above code is get the rows beween and including \n", "#the indices datetime(2014,12,8) and datetime(2014,12,12)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have a `datetime64` index, let's plot `'Max Wind SpeedKm/h'` again:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Spq5EKg0/mJrKT26aj278BDFY/E0dtEDZroxtNx/QHKTUJ7ACuudxudswrkw9eXwVv/x3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S/JYk/nC4C3TxFbg5xdrKNu2HyBGS1mYNoO/vWFs9g/PoDhgo7xgSz+7LET+MhnnsLFlSYaBpvA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\nt5gi6fSyMveFVhYAPgJGRMSGw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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "london['Max Wind SpeedKm/h'].plot(grid=True, figsize=(10,5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now it is much clearer that the worst winds were in mid February.\n", "\n", "### Task\n", "Use the code cell below to plot the values of `'Mean Humidity'` during spring (full months of March, April and May)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Now go back to the course step and mark it complete.**" ] } ], "metadata": { "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": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 1 }