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How to use mean method in pytest-benchmark
Best Python code snippet using pytest-benchmark
simplexample.py
Source:simplexample.py 
...17 ### Argentina Top 200 Charts18 danceability200 = argentina_top200[['date', 'danceability']]19 danceability200.set_index('date', inplace=True)20 danceability200 = danceability200.dropna()21 danceability200_mean = danceability200.groupby('date').mean()22 23 energy200 = argentina_top200[['date', 'energy']]24 energy200.set_index('date', inplace=True)25 energy200 = energy200.dropna()26 energy200_mean = energy200.groupby('date').mean()27 key200 = argentina_top200[['date', 'key']]28 key200.set_index('date', inplace=True)29 key200 = key200.dropna()30 key200_mean = key200.groupby('date').mean()31 loud200 = argentina_top200[['date', 'loudness']]32 loud200.set_index('date', inplace=True)33 loud200 = loud200.dropna()34 loud200_mean = loud200.groupby('date').mean()35 mode200 = argentina_top200[['date', 'mode']]36 mode200.set_index('date', inplace=True)37 mode200 = mode200.dropna()38 mode200_mean = mode200.groupby('date').mean()39 speech200 = argentina_top200[['date', 'speechiness']]40 speech200.set_index('date', inplace=True)41 speech200 = speech200.dropna()42 speech200_mean = speech200.groupby('date').mean()43 acou200 = argentina_top200[['date', 'acousticness']]44 acou200.set_index('date', inplace=True)45 acou200 = acou200.dropna()46 acou200_mean = acou200.groupby('date').mean()47 instr200 = argentina_top200[['date', 'instrumentalness']]48 instr200.set_index('date', inplace=True)49 instr200 = instr200.dropna()50 instr200_mean = instr200.groupby('date').mean()51 live200 = argentina_top200[['date', 'liveness']]52 live200.set_index('date', inplace=True)53 live200 = live200.dropna()54 live200_mean = live200.groupby('date').mean()55 valence200 = argentina_top200[['date', 'valence']]56 valence200.set_index('date', inplace=True)57 valence200 = valence200.dropna()58 valence200_mean = valence200.groupby('date').mean()59 tempo200 = argentina_top200[['date', 'tempo']]60 tempo200.set_index('date', inplace=True)61 tempo200 = tempo200.dropna()62 tempo200_mean = tempo200.groupby('date').mean()63 duration200 = argentina_top200[['date', 'duration_ms']]64 duration200.set_index('date', inplace=True)65 duration200 = duration200.dropna()66 duration200_mean = duration200.groupby('date').mean()67 x_vals=[danceability200_mean.index, energy200_mean.index,key200_mean.index,loud200_mean.index, mode200_mean.index,speech200_mean.index,acou200_mean.index,instr200_mean.index,live200_mean.index,valence200_mean.index,tempo200_mean.index,duration200_mean.index]68 y_vals=['danceability','energy','key','loudness','mode','speechiness','acousticness','instrumentalness','liveness','valence','tempo','duration_ms']69 return x_vals, y_vals, argentina_top20070def australia():71 # read csv data for each country into associated dataframe72 argentina = pd.read_csv('/workspace/plotyDashWebapp/SpotifyDataAnalysis/home/dash_apps/finished_apps/CSVFile/Australia.csv')73 # drop unnamed column from each dataframe74 argentina = argentina.drop(columns=['Unnamed: 0'])75 argentina['date'] = pd.to_datetime(argentina['date'], format='%Y-%m-%d')76 argentina['year'] = pd.DatetimeIndex(argentina['date']).year77 ## Seasonality in Argentina based on Top 200 and Viral 50 charts respectively78 argentina_top200 = argentina[argentina['chart'] == 'top200']79 argentina_viral50 = argentina[argentina['chart'] == 'viral50']80 ### Argentina Top 200 Charts81 danceability200 = argentina_top200[['date', 'danceability']]82 danceability200.set_index('date', inplace=True)83 danceability200 = danceability200.dropna()84 danceability200_mean = danceability200.groupby('date').mean()85 86 energy200 = argentina_top200[['date', 'energy']]87 energy200.set_index('date', inplace=True)88 energy200 = energy200.dropna()89 energy200_mean = energy200.groupby('date').mean()90 key200 = argentina_top200[['date', 'key']]91 key200.set_index('date', inplace=True)92 key200 = key200.dropna()93 key200_mean = key200.groupby('date').mean()94 loud200 = argentina_top200[['date', 'loudness']]95 loud200.set_index('date', inplace=True)96 loud200 = loud200.dropna()97 loud200_mean = loud200.groupby('date').mean()98 mode200 = argentina_top200[['date', 'mode']]99 mode200.set_index('date', inplace=True)100 mode200 = mode200.dropna()101 mode200_mean = mode200.groupby('date').mean()102 speech200 = argentina_top200[['date', 'speechiness']]103 speech200.set_index('date', inplace=True)104 speech200 = speech200.dropna()105 speech200_mean = speech200.groupby('date').mean()106 acou200 = argentina_top200[['date', 'acousticness']]107 acou200.set_index('date', inplace=True)108 acou200 = acou200.dropna()109 acou200_mean = acou200.groupby('date').mean()110 instr200 = argentina_top200[['date', 'instrumentalness']]111 instr200.set_index('date', inplace=True)112 instr200 = instr200.dropna()113 instr200_mean = instr200.groupby('date').mean()114 live200 = argentina_top200[['date', 'liveness']]115 live200.set_index('date', inplace=True)116 live200 = live200.dropna()117 live200_mean = live200.groupby('date').mean()118 valence200 = argentina_top200[['date', 'valence']]119 valence200.set_index('date', inplace=True)120 valence200 = valence200.dropna()121 valence200_mean = valence200.groupby('date').mean()122 tempo200 = argentina_top200[['date', 'tempo']]123 tempo200.set_index('date', inplace=True)124 tempo200 = tempo200.dropna()125 tempo200_mean = tempo200.groupby('date').mean()126 duration200 = argentina_top200[['date', 'duration_ms']]127 duration200.set_index('date', inplace=True)128 duration200 = duration200.dropna()129 duration200_mean = duration200.groupby('date').mean()130 x_vals=[danceability200_mean.index, energy200_mean.index,key200_mean.index,loud200_mean.index, mode200_mean.index,speech200_mean.index,acou200_mean.index,instr200_mean.index,live200_mean.index,valence200_mean.index,tempo200_mean.index,duration200_mean.index]131 y_vals=['danceability','energy','key','loudness','mode','speechiness','acousticness','instrumentalness','liveness','valence','tempo','duration_ms']132 return x_vals, y_vals, argentina_top200133def england():134 # read csv data for each country into associated dataframe135 argentina = pd.read_csv('/workspace/plotyDashWebapp/SpotifyDataAnalysis/home/dash_apps/finished_apps/CSVFile/England.csv')136 # drop unnamed column from each dataframe137 argentina = argentina.drop(columns=['Unnamed: 0'])138 argentina['date'] = pd.to_datetime(argentina['date'], format='%Y-%m-%d')139 argentina['year'] = pd.DatetimeIndex(argentina['date']).year140 ## Seasonality in Argentina based on Top 200 and Viral 50 charts respectively141 argentina_top200 = argentina[argentina['chart'] == 'top200']142 argentina_viral50 = argentina[argentina['chart'] == 'viral50']143 ### Argentina Top 200 Charts144 danceability200 = argentina_top200[['date', 'danceability']]145 danceability200.set_index('date', inplace=True)146 danceability200 = danceability200.dropna()147 danceability200_mean = danceability200.groupby('date').mean()148 149 energy200 = argentina_top200[['date', 'energy']]150 energy200.set_index('date', inplace=True)151 energy200 = energy200.dropna()152 energy200_mean = energy200.groupby('date').mean()153 key200 = argentina_top200[['date', 'key']]154 key200.set_index('date', inplace=True)155 key200 = key200.dropna()156 key200_mean = key200.groupby('date').mean()157 loud200 = argentina_top200[['date', 'loudness']]158 loud200.set_index('date', inplace=True)159 loud200 = loud200.dropna()160 loud200_mean = loud200.groupby('date').mean()161 mode200 = argentina_top200[['date', 'mode']]162 mode200.set_index('date', inplace=True)163 mode200 = mode200.dropna()164 mode200_mean = mode200.groupby('date').mean()165 speech200 = argentina_top200[['date', 'speechiness']]166 speech200.set_index('date', inplace=True)167 speech200 = speech200.dropna()168 speech200_mean = speech200.groupby('date').mean()169 acou200 = argentina_top200[['date', 'acousticness']]170 acou200.set_index('date', inplace=True)171 acou200 = acou200.dropna()172 acou200_mean = acou200.groupby('date').mean()173 instr200 = argentina_top200[['date', 'instrumentalness']]174 instr200.set_index('date', inplace=True)175 instr200 = instr200.dropna()176 instr200_mean = instr200.groupby('date').mean()177 live200 = argentina_top200[['date', 'liveness']]178 live200.set_index('date', inplace=True)179 live200 = live200.dropna()180 live200_mean = live200.groupby('date').mean()181 valence200 = argentina_top200[['date', 'valence']]182 valence200.set_index('date', inplace=True)183 valence200 = valence200.dropna()184 valence200_mean = valence200.groupby('date').mean()185 tempo200 = argentina_top200[['date', 'tempo']]186 tempo200.set_index('date', inplace=True)187 tempo200 = tempo200.dropna()188 tempo200_mean = tempo200.groupby('date').mean()189 duration200 = argentina_top200[['date', 'duration_ms']]190 duration200.set_index('date', inplace=True)191 duration200 = duration200.dropna()192 duration200_mean = duration200.groupby('date').mean()193 x_vals=[danceability200_mean.index, energy200_mean.index,key200_mean.index,loud200_mean.index, mode200_mean.index,speech200_mean.index,acou200_mean.index,instr200_mean.index,live200_mean.index,valence200_mean.index,tempo200_mean.index,duration200_mean.index]194 y_vals=['danceability','energy','key','loudness','mode','speechiness','acousticness','instrumentalness','liveness','valence','tempo','duration_ms']195 return x_vals, y_vals, argentina_top200196def usa():197 # read csv data for each country into associated dataframe198 argentina = pd.read_csv('/workspace/plotyDashWebapp/SpotifyDataAnalysis/home/dash_apps/finished_apps/CSVFile/USA.csv')199 # drop unnamed column from each dataframe200 argentina = argentina.drop(columns=['Unnamed: 0'])201 argentina['date'] = pd.to_datetime(argentina['date'], format='%Y-%m-%d')202 argentina['year'] = pd.DatetimeIndex(argentina['date']).year203 ## Seasonality in Argentina based on Top 200 and Viral 50 charts respectively204 argentina_top200 = argentina[argentina['chart'] == 'top200']205 argentina_viral50 = argentina[argentina['chart'] == 'viral50']206 ### Argentina Top 200 Charts207 danceability200 = argentina_top200[['date', 'danceability']]208 danceability200.set_index('date', inplace=True)209 danceability200 = danceability200.dropna()210 danceability200_mean = danceability200.groupby('date').mean()211 212 energy200 = argentina_top200[['date', 'energy']]213 energy200.set_index('date', inplace=True)214 energy200 = energy200.dropna()215 energy200_mean = energy200.groupby('date').mean()216 key200 = argentina_top200[['date', 'key']]217 key200.set_index('date', inplace=True)218 key200 = key200.dropna()219 key200_mean = key200.groupby('date').mean()220 loud200 = argentina_top200[['date', 'loudness']]221 loud200.set_index('date', inplace=True)222 loud200 = loud200.dropna()223 loud200_mean = loud200.groupby('date').mean()224 mode200 = argentina_top200[['date', 'mode']]225 mode200.set_index('date', inplace=True)226 mode200 = mode200.dropna()227 mode200_mean = mode200.groupby('date').mean()228 speech200 = argentina_top200[['date', 'speechiness']]229 speech200.set_index('date', inplace=True)230 speech200 = speech200.dropna()231 speech200_mean = speech200.groupby('date').mean()232 acou200 = argentina_top200[['date', 'acousticness']]233 acou200.set_index('date', inplace=True)234 acou200 = acou200.dropna()235 acou200_mean = acou200.groupby('date').mean()236 instr200 = argentina_top200[['date', 'instrumentalness']]237 instr200.set_index('date', inplace=True)238 instr200 = instr200.dropna()239 instr200_mean = instr200.groupby('date').mean()240 live200 = argentina_top200[['date', 'liveness']]241 live200.set_index('date', inplace=True)242 live200 = live200.dropna()243 live200_mean = live200.groupby('date').mean()244 valence200 = argentina_top200[['date', 'valence']]245 valence200.set_index('date', inplace=True)246 valence200 = valence200.dropna()247 valence200_mean = valence200.groupby('date').mean()248 tempo200 = argentina_top200[['date', 'tempo']]249 tempo200.set_index('date', inplace=True)250 tempo200 = tempo200.dropna()251 tempo200_mean = tempo200.groupby('date').mean()252 duration200 = argentina_top200[['date', 'duration_ms']]253 duration200.set_index('date', inplace=True)254 duration200 = duration200.dropna()255 duration200_mean = duration200.groupby('date').mean()256 x_vals=[danceability200_mean.index, energy200_mean.index,key200_mean.index,loud200_mean.index, mode200_mean.index,speech200_mean.index,acou200_mean.index,instr200_mean.index,live200_mean.index,valence200_mean.index,tempo200_mean.index,duration200_mean.index]257 y_vals=['danceability','energy','key','loudness','mode','speechiness','acousticness','instrumentalness','liveness','valence','tempo','duration_ms']258 return x_vals, y_vals, argentina_top200259def mexico():260 # read csv data for each country into associated dataframe261 argentina = pd.read_csv('/workspace/plotyDashWebapp/SpotifyDataAnalysis/home/dash_apps/finished_apps/CSVFile/Mexico.csv')262 # drop unnamed column from each dataframe263 argentina = argentina.drop(columns=['Unnamed: 0'])264 argentina['date'] = pd.to_datetime(argentina['date'], format='%Y-%m-%d')265 argentina['year'] = pd.DatetimeIndex(argentina['date']).year266 ## Seasonality in Argentina based on Top 200 and Viral 50 charts respectively267 argentina_top200 = argentina[argentina['chart'] == 'top200']268 argentina_viral50 = argentina[argentina['chart'] == 'viral50']269 ### Argentina Top 200 Charts270 danceability200 = argentina_top200[['date', 'danceability']]271 danceability200.set_index('date', inplace=True)272 danceability200 = danceability200.dropna()273 danceability200_mean = danceability200.groupby('date').mean()274 275 energy200 = argentina_top200[['date', 'energy']]276 energy200.set_index('date', inplace=True)277 energy200 = energy200.dropna()278 energy200_mean = energy200.groupby('date').mean()279 key200 = argentina_top200[['date', 'key']]280 key200.set_index('date', inplace=True)281 key200 = key200.dropna()282 key200_mean = key200.groupby('date').mean()283 loud200 = argentina_top200[['date', 'loudness']]284 loud200.set_index('date', inplace=True)285 loud200 = loud200.dropna()286 loud200_mean = loud200.groupby('date').mean()287 mode200 = argentina_top200[['date', 'mode']]288 mode200.set_index('date', inplace=True)289 mode200 = mode200.dropna()290 mode200_mean = mode200.groupby('date').mean()291 speech200 = argentina_top200[['date', 'speechiness']]292 speech200.set_index('date', inplace=True)293 speech200 = speech200.dropna()294 speech200_mean = speech200.groupby('date').mean()295 acou200 = argentina_top200[['date', 'acousticness']]296 acou200.set_index('date', inplace=True)297 acou200 = acou200.dropna()298 acou200_mean = acou200.groupby('date').mean()299 instr200 = argentina_top200[['date', 'instrumentalness']]300 instr200.set_index('date', inplace=True)301 instr200 = instr200.dropna()302 instr200_mean = instr200.groupby('date').mean()303 live200 = argentina_top200[['date', 'liveness']]304 live200.set_index('date', inplace=True)305 live200 = live200.dropna()306 live200_mean = live200.groupby('date').mean()307 valence200 = argentina_top200[['date', 'valence']]308 valence200.set_index('date', inplace=True)309 valence200 = valence200.dropna()310 valence200_mean = valence200.groupby('date').mean()311 tempo200 = argentina_top200[['date', 'tempo']]312 tempo200.set_index('date', inplace=True)313 tempo200 = tempo200.dropna()314 tempo200_mean = tempo200.groupby('date').mean()315 duration200 = argentina_top200[['date', 'duration_ms']]316 duration200.set_index('date', inplace=True)317 duration200 = duration200.dropna()318 duration200_mean = duration200.groupby('date').mean()319 x_vals=[danceability200_mean.index, energy200_mean.index,key200_mean.index,loud200_mean.index, mode200_mean.index,speech200_mean.index,acou200_mean.index,instr200_mean.index,live200_mean.index,valence200_mean.index,tempo200_mean.index,duration200_mean.index]320 y_vals=['danceability','energy','key','loudness','mode','speechiness','acousticness','instrumentalness','liveness','valence','tempo','duration_ms']321 return x_vals, y_vals, argentina_top200322def spain():323 # read csv data for each country into associated dataframe324 argentina = pd.read_csv('/workspace/plotyDashWebapp/SpotifyDataAnalysis/home/dash_apps/finished_apps/CSVFile/Spain.csv')325 # drop unnamed column from each dataframe326 argentina = argentina.drop(columns=['Unnamed: 0'])327 argentina['date'] = pd.to_datetime(argentina['date'], format='%Y-%m-%d')328 argentina['year'] = pd.DatetimeIndex(argentina['date']).year329 ## Seasonality in Argentina based on Top 200 and Viral 50 charts respectively330 argentina_top200 = argentina[argentina['chart'] == 'top200']331 argentina_viral50 = argentina[argentina['chart'] == 'viral50']332 ### Argentina Top 200 Charts333 danceability200 = argentina_top200[['date', 'danceability']]334 danceability200.set_index('date', inplace=True)335 danceability200 = danceability200.dropna()336 danceability200_mean = danceability200.groupby('date').mean()337 338 energy200 = argentina_top200[['date', 'energy']]339 energy200.set_index('date', inplace=True)340 energy200 = energy200.dropna()341 energy200_mean = energy200.groupby('date').mean()342 key200 = argentina_top200[['date', 'key']]343 key200.set_index('date', inplace=True)344 key200 = key200.dropna()345 key200_mean = key200.groupby('date').mean()346 loud200 = argentina_top200[['date', 'loudness']]347 loud200.set_index('date', inplace=True)348 loud200 = loud200.dropna()349 loud200_mean = loud200.groupby('date').mean()350 mode200 = argentina_top200[['date', 'mode']]351 mode200.set_index('date', inplace=True)352 mode200 = mode200.dropna()353 mode200_mean = mode200.groupby('date').mean()354 speech200 = argentina_top200[['date', 'speechiness']]355 speech200.set_index('date', inplace=True)356 speech200 = speech200.dropna()357 speech200_mean = speech200.groupby('date').mean()358 acou200 = argentina_top200[['date', 'acousticness']]359 acou200.set_index('date', inplace=True)360 acou200 = acou200.dropna()361 acou200_mean = acou200.groupby('date').mean()362 instr200 = argentina_top200[['date', 'instrumentalness']]363 instr200.set_index('date', inplace=True)364 instr200 = instr200.dropna()365 instr200_mean = instr200.groupby('date').mean()366 live200 = argentina_top200[['date', 'liveness']]367 live200.set_index('date', inplace=True)368 live200 = live200.dropna()369 live200_mean = live200.groupby('date').mean()370 valence200 = argentina_top200[['date', 'valence']]371 valence200.set_index('date', inplace=True)372 valence200 = valence200.dropna()373 valence200_mean = valence200.groupby('date').mean()374 tempo200 = argentina_top200[['date', 'tempo']]375 tempo200.set_index('date', inplace=True)376 tempo200 = tempo200.dropna()377 tempo200_mean = tempo200.groupby('date').mean()378 duration200 = argentina_top200[['date', 'duration_ms']]379 duration200.set_index('date', inplace=True)380 duration200 = duration200.dropna()381 duration200_mean = duration200.groupby('date').mean()382 x_vals=[danceability200_mean.index, energy200_mean.index,key200_mean.index,loud200_mean.index, mode200_mean.index,speech200_mean.index,acou200_mean.index,instr200_mean.index,live200_mean.index,valence200_mean.index,tempo200_mean.index,duration200_mean.index]383 y_vals=['danceability','energy','key','loudness','mode','speechiness','acousticness','instrumentalness','liveness','valence','tempo','duration_ms']...
jquery.meanmenu.js
Source:jquery.meanmenu.js
1/*!2* jQuery meanMenu v2.0.83* @Copyright (C) 2012-2014 Chris Wharton @ MeanThemes (https://github.com/meanthemes/meanMenu)4*5*/6/*7* This program is free software: you can redistribute it and/or modify8* it under the terms of the GNU General Public License as published by9* the Free Software Foundation, either version 3 of the License, or10* (at your option) any later version.11*12* THIS SOFTWARE AND DOCUMENTATION IS PROVIDED "AS IS," AND COPYRIGHT13* HOLDERS MAKE NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED,14* INCLUDING BUT NOT LIMITED TO, WARRANTIES OF MERCHANTABILITY OR15* FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF THE SOFTWARE16* OR DOCUMENTATION WILL NOT INFRINGE ANY THIRD PARTY PATENTS,17* COPYRIGHTS, TRADEMARKS OR OTHER RIGHTS.COPYRIGHT HOLDERS WILL NOT18* BE LIABLE FOR ANY DIRECT, INDIRECT, SPECIAL OR CONSEQUENTIAL19* DAMAGES ARISING OUT OF ANY USE OF THE SOFTWARE OR DOCUMENTATION.20*21* You should have received a copy of the GNU General Public License22* along with this program. If not, see <http://gnu.org/licenses/>.23*24* Find more information at http://www.meanthemes.com/plugins/meanmenu/25*26*/27(function ($) {28 "use strict";29 $.fn.meanmenu = function (options) {30 var defaults = {31 meanMenuTarget: jQuery(this), // Target the current HTML markup you wish to replace32 meanMenuContainer: '.mobile-menu-area .container', // Choose where meanmenu will be placed within the HTML33 meanMenuClose: "X", // single character you want to represent the close menu button34 meanMenuCloseSize: "18px", // set font size of close button35 meanMenuOpen: "<span /><span /><span />", // text/markup you want when menu is closed36 meanRevealPosition: "right", // left right or center positions37 meanRevealPositionDistance: "0", // Tweak the position of the menu38 meanRevealColour: "", // override CSS colours for the reveal background39 meanScreenWidth: "767", // set the screen width you want meanmenu to kick in at40 meanNavPush: "", // set a height here in px, em or % if you want to budge your layout now the navigation is missing.41 meanShowChildren: true, // true to show children in the menu, false to hide them42 meanExpandableChildren: true, // true to allow expand/collapse children43 meanExpand: "+", // single character you want to represent the expand for ULs44 meanContract: "-", // single character you want to represent the contract for ULs45 meanRemoveAttrs: false, // true to remove classes and IDs, false to keep them46 onePage: false, // set to true for one page sites47 meanDisplay: "block", // override display method for table cell based layouts e.g. table-cell48 removeElements: "" // set to hide page elements49 };50 options = $.extend(defaults, options);51 // get browser width52 var currentWidth = window.innerWidth || document.documentElement.clientWidth;53 return this.each(function () {54 var meanMenu = options.meanMenuTarget;55 var meanContainer = options.meanMenuContainer;56 var meanMenuClose = options.meanMenuClose;57 var meanMenuCloseSize = options.meanMenuCloseSize;58 var meanMenuOpen = options.meanMenuOpen;59 var meanRevealPosition = options.meanRevealPosition;60 var meanRevealPositionDistance = options.meanRevealPositionDistance;61 var meanRevealColour = options.meanRevealColour;62 var meanScreenWidth = options.meanScreenWidth;63 var meanNavPush = options.meanNavPush;64 var meanRevealClass = ".meanmenu-reveal";65 var meanShowChildren = options.meanShowChildren;66 var meanExpandableChildren = options.meanExpandableChildren;67 var meanExpand = options.meanExpand;68 var meanContract = options.meanContract;69 var meanRemoveAttrs = options.meanRemoveAttrs;70 var onePage = options.onePage;71 var meanDisplay = options.meanDisplay;72 var removeElements = options.removeElements;73 //detect known mobile/tablet usage74 var isMobile = false;75 if ( (navigator.userAgent.match(/iPhone/i)) || (navigator.userAgent.match(/iPod/i)) || (navigator.userAgent.match(/iPad/i)) || (navigator.userAgent.match(/Android/i)) || (navigator.userAgent.match(/Blackberry/i)) || (navigator.userAgent.match(/Windows Phone/i)) ) {76 isMobile = true;77 }78 if ( (navigator.userAgent.match(/MSIE 8/i)) || (navigator.userAgent.match(/MSIE 7/i)) ) {79 // add scrollbar for IE7 & 8 to stop breaking resize function on small content sites80 jQuery('html').css("overflow-y" , "scroll");81 }82 var meanRevealPos = "";83 var meanCentered = function() {84 if (meanRevealPosition === "center") {85 var newWidth = window.innerWidth || document.documentElement.clientWidth;86 var meanCenter = ( (newWidth/2)-22 )+"px";87 meanRevealPos = "left:" + meanCenter + ";right:auto;";88 if (!isMobile) {89 jQuery('.meanmenu-reveal').css("left",meanCenter);90 } else {91 jQuery('.meanmenu-reveal').animate({92 left: meanCenter93 });94 }95 }96 };97 var menuOn = false;98 var meanMenuExist = false;99 if (meanRevealPosition === "right") {100 meanRevealPos = "right:" + meanRevealPositionDistance + ";left:auto;";101 }102 if (meanRevealPosition === "left") {103 meanRevealPos = "left:" + meanRevealPositionDistance + ";right:auto;";104 }105 // run center function106 meanCentered();107 // set all styles for mean-reveal108 var $navreveal = "";109 var meanInner = function() {110 // get last class name111 if (jQuery($navreveal).is(".meanmenu-reveal.meanclose")) {112 $navreveal.html(meanMenuClose);113 } else {114 $navreveal.html(meanMenuOpen);115 }116 };117 // re-instate original nav (and call this on window.width functions)118 var meanOriginal = function() {119 jQuery('.mean-bar,.mean-push').remove();120 jQuery(meanContainer).removeClass("mean-container");121 jQuery(meanMenu).css('display', meanDisplay);122 menuOn = false;123 meanMenuExist = false;124 jQuery(removeElements).removeClass('mean-remove');125 };126 // navigation reveal127 var showMeanMenu = function() {128 var meanStyles = "background:"+meanRevealColour+";color:"+meanRevealColour+";"+meanRevealPos;129 if (currentWidth <= meanScreenWidth) {130 jQuery(removeElements).addClass('mean-remove');131 meanMenuExist = true;132 // add class to body so we don't need to worry about media queries here, all CSS is wrapped in '.mean-container'133 jQuery(meanContainer).addClass("mean-container");134 jQuery('.mean-container').prepend('<div class="mean-bar"><a href="#nav" class="meanmenu-reveal" style="'+meanStyles+'">Show Navigation</a><nav class="mean-nav"></nav></div>');135 //push meanMenu navigation into .mean-nav136 var meanMenuContents = jQuery(meanMenu).html();137 jQuery('.mean-nav').html(meanMenuContents);138 // remove all classes from EVERYTHING inside meanmenu nav139 if(meanRemoveAttrs) {140 jQuery('nav.mean-nav ul, nav.mean-nav ul *').each(function() {141 // First check if this has mean-remove class142 if (jQuery(this).is('.mean-remove')) {143 jQuery(this).attr('class', 'mean-remove');144 } else {145 jQuery(this).removeAttr("class");146 }147 jQuery(this).removeAttr("id");148 });149 }150 // push in a holder div (this can be used if removal of nav is causing layout issues)151 jQuery(meanMenu).before('<div class="mean-push" />');152 jQuery('.mean-push').css("margin-top",meanNavPush);153 // hide current navigation and reveal mean nav link154 jQuery(meanMenu).hide();155 jQuery(".meanmenu-reveal").show();156 // turn 'X' on or off157 jQuery(meanRevealClass).html(meanMenuOpen);158 $navreveal = jQuery(meanRevealClass);159 //hide mean-nav ul160 jQuery('.mean-nav ul').hide();161 // hide sub nav162 if(meanShowChildren) {163 // allow expandable sub nav(s)164 if(meanExpandableChildren){165 jQuery('.mean-nav ul ul').each(function() {166 if(jQuery(this).children().length){167 jQuery(this,'li:first').parent().append('<a class="mean-expand" href="#" style="font-size: '+ meanMenuCloseSize +'">'+ meanExpand +'</a>');168 }169 });170 jQuery('.mean-expand').on("click",function(e){171 e.preventDefault();172 if (jQuery(this).hasClass("mean-clicked")) {173 jQuery(this).text(meanExpand);174 jQuery(this).prev('ul').slideUp(300, function(){});175 } else {176 jQuery(this).text(meanContract);177 jQuery(this).prev('ul').slideDown(300, function(){});178 }179 jQuery(this).toggleClass("mean-clicked");180 });181 } else {182 jQuery('.mean-nav ul ul').show();183 }184 } else {185 jQuery('.mean-nav ul ul').hide();186 }187 // add last class to tidy up borders188 jQuery('.mean-nav ul li').last().addClass('mean-last');189 $navreveal.removeClass("meanclose");190 jQuery($navreveal).click(function(e){191 e.preventDefault();192 if( menuOn === false ) {193 $navreveal.css("text-align", "center");194 $navreveal.css("text-indent", "0");195 $navreveal.css("font-size", meanMenuCloseSize);196 jQuery('.mean-nav ul:first').slideDown();197 menuOn = true;198 } else {199 jQuery('.mean-nav ul:first').slideUp();200 menuOn = false;201 }202 $navreveal.toggleClass("meanclose");203 meanInner();204 jQuery(removeElements).addClass('mean-remove');205 });206 // for one page websites, reset all variables...207 if ( onePage ) {208 jQuery('.mean-nav ul > li > a:first-child').on( "click" , function () {209 jQuery('.mean-nav ul:first').slideUp();210 menuOn = false;211 jQuery($navreveal).toggleClass("meanclose").html(meanMenuOpen);212 });213 }214 } else {215 meanOriginal();216 }217 };218 if (!isMobile) {219 // reset menu on resize above meanScreenWidth220 jQuery(window).resize(function () {221 currentWidth = window.innerWidth || document.documentElement.clientWidth;222 if (currentWidth > meanScreenWidth) {223 meanOriginal();224 } else {225 meanOriginal();226 }227 if (currentWidth <= meanScreenWidth) {228 showMeanMenu();229 meanCentered();230 } else {231 meanOriginal();232 }233 });234 }235 jQuery(window).resize(function () {236 // get browser width237 currentWidth = window.innerWidth || document.documentElement.clientWidth;238 if (!isMobile) {239 meanOriginal();240 if (currentWidth <= meanScreenWidth) {241 showMeanMenu();242 meanCentered();243 }244 } else {245 meanCentered();246 if (currentWidth <= meanScreenWidth) {247 if (meanMenuExist === false) {248 showMeanMenu();249 }250 } else {251 meanOriginal();252 }253 }254 });255 // run main menuMenu function on load256 showMeanMenu();257 });258 };259 260 /*--261 Mobile Menu262 ------------------------*/263 $('.mobile-menu nav').meanmenu({264 meanScreenWidth: "990",265 meanMenuContainer: ".mobile-menu",266 onePage: false,267 }); 268 ...
z-score.py
Source:z-score.py
...1213## code to find the mean of 100 data points 1000 times 14#function to get the mean of the given data samples15# pass the number of data points you want as counter16def random_set_of_mean(counter):17 dataset = []18 for i in range(0, counter):19 random_index= random.randint(0,len(data)-1)20 value = data[random_index]21 dataset.append(value)22 mean = statistics.mean(dataset)23 return mean242526# Function to get the mean of 100 data sets27mean_list = []28for i in range(0,1000):29 set_of_means= random_set_of_mean(100)30 mean_list.append(set_of_means)313233## calculating mean and standard_deviation of the sampling distribution.34std_deviation = statistics.stdev(mean_list)35mean = statistics.mean(mean_list)36print("mean of sampling distribution:- ",mean)37print("Standard deviation of sampling distribution:- ", std_deviation)38394041## findig the standard deviation starting and ending values42first_std_deviation_start, first_std_deviation_end = mean-std_deviation, mean+std_deviation43second_std_deviation_start, second_std_deviation_end = mean-(2*std_deviation), mean+(2*std_deviation)44third_std_deviation_start, third_std_deviation_end = mean-(3*std_deviation), mean+(3*std_deviation)45# print("std1",first_std_deviation_start, first_std_deviation_end)46# print("std2",second_std_deviation_start, second_std_deviation_end)47# print("std3",third_std_deviation_start,third_std_deviation_end)4849505152# # finding the mean of THE STUDENTS WHO GAVE EXTRA TIME TO MATH LAB and plotting on graph53df = pd.read_csv("School_1_Sample.csv")54data = df["Math_score"].tolist()55mean_of_sample1 = statistics.mean(data)56print("Mean of sample1:- ",mean_of_sample1)57fig = ff.create_distplot([mean_list], ["student marks"], show_hist=False)58fig.add_trace(go.Scatter(x=[mean, mean], y=[0, 0.17], mode="lines", name="MEAN"))59fig.add_trace(go.Scatter(x=[mean_of_sample1, mean_of_sample1], y=[0, 0.17], mode="lines", name="MEAN OF STUDENTS WHO HAD MATH LABS"))60fig.add_trace(go.Scatter(x=[first_std_deviation_end, first_std_deviation_end], y=[0, 0.17], mode="lines", name="STANDARD DEVIATION 1 END"))61fig.add_trace(go.Scatter(x=[second_std_deviation_end, second_std_deviation_end], y=[0, 0.17], mode="lines", name="STANDARD DEVIATION 2 END"))62fig.add_trace(go.Scatter(x=[third_std_deviation_end, third_std_deviation_end], y=[0, 0.17], mode="lines", name="STANDARD DEVIATION 3 END"))63fig.show()6465666768# #finding the mean of the STUDENTS WHO USED MATH PRACTISE APP and plotting it on the plot.69df = pd.read_csv("School_2_Sample.csv")70data = df["Math_score"].tolist()71mean_of_sample2 = statistics.mean(data)72print("mean of sample 2:- ",mean_of_sample2)73fig = ff.create_distplot([mean_list], ["student marks"], show_hist=False)74fig.add_trace(go.Scatter(x=[mean, mean], y=[0, 0.17], mode="lines", name="MEAN"))75fig.add_trace(go.Scatter(x=[mean_of_sample2, mean_of_sample2], y=[0, 0.17], mode="lines", name="MEAN OF STUDENTS WHO USED THE APP"))76fig.add_trace(go.Scatter(x=[first_std_deviation_end, first_std_deviation_end], y=[0, 0.17], mode="lines", name="STANDARD DEVIATION 1 END"))77fig.add_trace(go.Scatter(x=[second_std_deviation_end, second_std_deviation_end], y=[0, 0.17], mode="lines", name="STANDARD DEVIATION 2 END"))78fig.add_trace(go.Scatter(x=[third_std_deviation_end, third_std_deviation_end], y=[0, 0.17], mode="lines", name="STANDARD DEVIATION 3 END"))79fig.show()808182# finding the mean of the STUDENTS WHO WERE ENFORCED WITH REGISTERS and plotting it on the plot.83df = pd.read_csv("School_3_Sample.csv")84data = df["Math_score"].tolist()85mean_of_sample3 = statistics.mean(data)86print("mean of sample3:- ",mean_of_sample3)87fig = ff.create_distplot([mean_list], ["student marks"], show_hist=False)88fig.add_trace(go.Scatter(x=[mean, mean], y=[0, 0.17], mode="lines", name="MEAN"))89fig.add_trace(go.Scatter(x=[mean_of_sample3, mean_of_sample3], y=[0, 0.17], mode="lines", name="MEAN OF STUDNETS WHO WERE ENFORCED WITH MATH REGISTERS"))90fig.add_trace(go.Scatter(x=[second_std_deviation_end, second_std_deviation_end], y=[0, 0.17], mode="lines", name="STANDARD DEVIATION 2 END"))91fig.add_trace(go.Scatter(x=[third_std_deviation_end, third_std_deviation_end], y=[0, 0.17], mode="lines", name="STANDARD DEVIATION 3 END"))92fig.show()939495#finding the z score 96z_score = (mean - mean_of_sample2)/std_deviation
...
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