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How to use calculate_percentages method in localstack
Best Python code snippet using localstack_python
data_preparation_helper_functions.py
Source:data_preparation_helper_functions.py 
1# -*- coding: utf-8 -*-2"""3------ What is this file? ------4This script contains some helper functions that are used in the scripts found under src/data_preparation.5The unit tests for these functions can be found at:6 tests/unit_tests/helper_functions/test_data_preparation_helper_functions.py7"""8#%% --- Import Required Packages ---9import pandas as pd10import matplotlib.pyplot as plt11import seaborn as sns12#%% --- Set proper directory to assure integration with doit ---13# abspath = os.path.abspath(__file__)14# dname = os.path.dirname(abspath)15# os.chdir(dname)16#%% --- FUNCTION: sample_and_read_from_df ---17 # --- Main Function --- #18 19def sample_and_read_from_df(dataframe, sample_size):20 """21 Sample n(sample_size) rows from the dataset(dataframe). Print out the samples in the console in a column-by-column basis.22 Keyword arguments:23 dataframe -- Accepts a pandas dataframe.24 sample-size -- Accepts an integer. (Default 10.)25 """26 valerror_text = "dataframe must be type pd.DataFrame, got {}".format(type(dataframe))27 if not isinstance(dataframe, pd.DataFrame):28 raise ValueError(valerror_text)29 30 valerror_text = "sample_size must be type int, got {}".format(type(sample_size))31 if not isinstance(sample_size, int):32 raise ValueError(valerror_text)33 34 index_error_text = ("dataframe length must be larger than or equal to sample_size. "35 "dataframe length is {}, sample_size is {}").format(len(dataframe), sample_size)36 if not len(dataframe) >= sample_size:37 raise IndexError(index_error_text)38 39 sample = dataframe.sample(sample_size)40 sample_columns = sample.columns41 for column in sample_columns:42 print("Commencing with " + column + " column of the dataframe")43 print("")44 for i in range(0,len(sample)):45 selection = sample.iloc[i]46 print(str(selection[column]).encode("utf-8"))47 print("") 48 49#%% --- FUNCTION: report_null_values ---50 # --- Helper Functions ---51 52def is_null_values_dataframe(dataframe):53 """54 Checks if a dataframe is null_values_dataframe.55 Parameters56 ----------57 dataframe : pandas.DataFrame58 Returns59 -------60 Returns Boolean True if the dataframe is a null_values_dataframe.61 Returns Boolean False if the dataframe is not a null_values_dataframe62 """63 valerror_text = "dataframe must be type pd.DataFrame, got {}".format(type(dataframe))64 if not isinstance(dataframe, pd.DataFrame):65 raise ValueError(valerror_text)66 67 is_null_values_dataframe = False68 69 if "null_count" in dataframe.columns:70 is_null_values_dataframe = True71 72 return is_null_values_dataframe73def is_extended_null_values_dataframe(null_values_dataframe):74 """75 Checks if dataframe is a extended null_values_dataframe76 Parameters77 ----------78 null_values_dataframe : pandas.DataFrame79 Returns80 -------81 Returns Boolean True if the dataframe is a extended null_values_dataframe.82 Returns Boolean False if the dataframe is not a extended null_values_dataframe83 """84 valerror_text = "dataframe must be type pd.DataFrame, got {}".format(type(null_values_dataframe))85 if not isinstance(null_values_dataframe, pd.DataFrame):86 raise ValueError(valerror_text)87 88 valerror_text = "dataframe does not contain null_count information."89 if not is_null_values_dataframe(null_values_dataframe):90 raise ValueError(valerror_text)91 92 is_extended_null_values_dataframe = False93 94 if "null_percentage" in null_values_dataframe.columns:95 is_extended_null_values_dataframe = True96 97 return is_extended_null_values_dataframe98def plot_null_values_bar_chart(null_values_dataframe):99 """100 Creates a bar chart from a null_values_dataframe.101 If the null_values_dataframe is an extended null_values_dataframe,102 creates a double bar chart view which displays percentage information.103 104 Parameters105 ----------106 null_values_dataframe : pandas.DataFrame.107 Returns108 -------109 Returns a matplotlib.pyplot.Figure object.110 """111 112 valerror_text = "dataframe must be type pd.DataFrame, got {}".format(type(null_values_dataframe))113 if not isinstance(null_values_dataframe, pd.DataFrame):114 raise ValueError(valerror_text)115 116 valerror_text = "dataframe does not contain null_count information."117 if not is_null_values_dataframe(null_values_dataframe):118 raise ValueError(valerror_text)119 120 fig = plt.Figure(figsize = (9.60,7.20))121 122 # Ax creating is wrapped inside if-else statement because creating123 #two instances of ax_1 DOES NOT override each other.124 if is_extended_null_values_dataframe(null_values_dataframe):125 ax_1 = fig.add_subplot(1,2,1)126 ax_2 = fig.add_subplot(1,2,2)127 128 else:129 ax_1 = fig.add_subplot(1,1,1)130 131 labels = list(null_values_dataframe.index)132 133 from numpy import arange134 bar_positions = arange(len(labels)) + 1135 136 bar_heights_1 = null_values_dataframe.loc[:,"null_count"]137 138 if is_extended_null_values_dataframe(null_values_dataframe):139 bar_heights_2 = null_values_dataframe.loc[:,"null_percentage"]140 141 ax_1.bar(bar_positions, bar_heights_1,142 width = 0.7,143 align = "center")144 145 ax_1.set_xticks(bar_positions)146 ax_1.set_xticklabels(labels, rotation = 90)147 148 if is_extended_null_values_dataframe(null_values_dataframe):149 ax_2.bar(bar_positions, bar_heights_2,150 width = 0.7,151 align = "center")152 153 ax_2.set_xticks(bar_positions)154 ax_2.set_xticklabels(labels, rotation = 90)155 156 plt.show()157 return fig158def plot_null_values_matrix(dataframe):159 """160 161 Parameters162 ----------163 dataframe : TYPE164 DESCRIPTION.165 Returns166 -------167 fig : TYPE168 DESCRIPTION.169 """170 171 valerror_text = "dataframe must be type pd.DataFrame, got {}".format(type(dataframe))172 if not isinstance(dataframe, pd.DataFrame):173 raise ValueError(valerror_text)174 175 #Create a boolean dataframe based on whether values are null or not176 df_null = dataframe.isnull()177 #create a heatmap of the boolean dataframe178 plot_object = sns.heatmap(df_null,179 cbar = False)180 181 plt.xticks(rotation=90, size='x-large')182 183 fig = plot_object.get_figure()184 return fig 185 186 # --- Subfunction : calculate_null_values ---187 188def calculate_null_values(dataframe, calculate_percentages = True):189 """190 191 Calculates the null values in the given dataframe in a column-by-column192 basis. Returns a dataframe whose index is column names of dataframe and whose193 values are the null value numbers.194 195 If report_percentages is True, also calculates of much of each column196 is made out of null values.197 Parameters198 ----------199 dataframe : pandas.DataFrame.200 calculate_percentages : Boolean value, optional201 Sets whether the null value percentage of columns will be calculated.202 The default is True.203 Returns204 -------205 null_values_report : pandas.DataFrame.206 A pandas.DataFrame that encodes information about207 null values in the original dataframe in a column-by-column basis.208 209 """210 valerror_text = "dataframe must be type pd.DataFrame, got {}".format(type(dataframe))211 if not isinstance(dataframe, pd.DataFrame):212 raise ValueError(valerror_text)213 214 valerror_text = "calculate_percentages must be type boolean True or False, got {}".format(type(calculate_percentages))215 if calculate_percentages not in [True, False]:216 raise ValueError(valerror_text)217 218 null_counts = dataframe.isnull().sum()219 220 if calculate_percentages == True:221 #Divide null count by total length of each col to find a percentage222 null_counts_pct = (null_counts / dataframe.shape[0]) * 100223 null_values_report = pd.concat([null_counts, null_counts_pct],224 axis = 1)225 null_values_report.rename(columns = {0:"null_count",1:"null_percentage"},226 inplace = True)227 else:228 null_values_report = null_counts.rename("null_count").to_frame()229 230 return null_values_report231 # --- Subfunction : print_null_values ---232 233def print_null_values(null_values_dataframe):234 """235 236 Prints out into the console a formatted message that explains237 a null_values_dataframe.238 239 Parameters240 ----------241 null_values_dataframe : pandas.DataFrame.242 A pandas.DataFrame that is produced as the result of calling243 calculate_null_values() on a dataframe. Has to contain a column244 called "null_count" at minimum.245 246 Returns247 -------248 None.249 """250 251 valerror_text = "dataframe must be type pd.DataFrame, got {}".format(type(null_values_dataframe))252 if not isinstance(null_values_dataframe, pd.DataFrame):253 raise ValueError(valerror_text)254 255 valerror_text = "dataframe does not contain null_count information."256 if not is_null_values_dataframe(null_values_dataframe):257 raise ValueError(valerror_text)258 259 if is_extended_null_values_dataframe(null_values_dataframe):260 for column in null_values_dataframe.index:261 column_null_count = str(null_values_dataframe.loc[column,"null_count"])262 print("Column {} has {} null values.".format(column,column_null_count))263 column_null_percentage = str(null_values_dataframe.loc[column, "null_percentage"])264 print("{} percent of column {} is null values.".format(column_null_percentage,column))265 else:266 for column in null_values_dataframe.index:267 column_null_count = str(null_values_dataframe.loc[column,"null_count"])268 print("Column {} has {} null values.".format(column,column_null_count))269 270 # --- Subfunction : visualize_null_values ---271 272def visualize_null_values(null_values_dataframe, kind = "bar_chart"):273 """274 Returns a visual representation of a given null_values_dataframe.275 Parameters276 ----------277 null_values_dataframe : pandas.DataFrame.278 A pandas.DataFrame that is produced as the result of calling279 calculate_null_values() on a dataframe. Has to contain a column280 called "null_count" at minimum.281 282 kind : One of the following strings "bar_chart", "matrix", "heatmap"283 The default is "bar_chart". "matrix" and "heatmap" is not implemented yet.284 Returns285 -------286 Returns a matplotlib.pyplot.Figure object.287 """288 valerror_text = "dataframe must be type pd.DataFrame, got {}".format(type(null_values_dataframe))289 if not isinstance(null_values_dataframe, pd.DataFrame):290 raise ValueError(valerror_text)291 292 valerror_text = "dataframe does not contain null_count information."293 if not is_null_values_dataframe(null_values_dataframe):294 raise ValueError(valerror_text)295 296 accepted_kinds = ["bar_chart", "matrix", "heatmap"]297 valerror_text = "Parameter kind must be a string and one of bar_chart, matrix or heatmap. Got \"{}\" as type {}.".format(str(kind), type(kind))298 if str(kind) not in accepted_kinds:299 raise ValueError(valerror_text)300 301 if kind == "bar_chart":302 plot = plot_null_values_bar_chart(null_values_dataframe)303 304 elif kind == "matrix":305 raise NotImplementedError306 307 elif kind == "heatmap": 308 raise NotImplementedError309 310 return plot311 # --- Main function: report_null_values ---312def report_null_values(dataframe, calculate_percentages = True,313 visualize_results = False, print_results = False):314 """315 Composes a report about the null values within the given dataframe.316 Parameters317 ----------318 dataframe : pandas.DataFrame.319 A pandas.DataFrame that will be evaluated for nullity information.320 321 calculate_percentages: Boolean True or False.322 Related to the subfuction calculate_percentages. Determines whether or not323 percentages related to nullity will be calculated.324 325 visualize_results: Boolean True or False.326 Related to the subfunction visualize_null_values. Determines whether or not327 this function will return a matplotlib.Figure object.328 329 print_results: Boolean True or False.330 Related to the subfunction print_null_values. Determines whether or not331 this function will print out a formatted report.332 333 Returns334 -------335 Depending on the arguments passed, returns one of the following:336 - pandas.DataFrame object337 - matplotlib.Figure object338 - None (print out a report)339 """340 341 valerror_text = "dataframe must be type pd.DataFrame, got {}".format(type(dataframe))342 if not isinstance(dataframe, pd.DataFrame):343 raise ValueError(valerror_text)344 345 parameters = [calculate_percentages, visualize_results, print_results]346 for parameter in parameters:347 valerror_text = "{} must be type boolean True or False, got {}".format(parameter, type(parameter))348 if parameter not in [True, False]:349 raise ValueError(valerror_text)350 351 valerror_text = "Parameters visualize_results and print_results cannot be both boolean True."352 if visualize_results == True and print_results == True:353 raise ValueError(valerror_text)354 355 null_values_dataframe = calculate_null_values(dataframe, calculate_percentages = calculate_percentages)356 if visualize_results == True:357 plot = visualize_null_values(null_values_dataframe)358 return plot359 360 elif print_results == True:361 print_null_values(null_values_dataframe)362 return363 364 else:365 return null_values_dataframe366 ...
utils.py
Source:utils.py
...3from django.db import connection4from django.db.models import Count5from referendum.models import ActiveVote6from referendum import tasks7def calculate_percentages(counts, digits=2):8 '''9 Calculate percentages, given a list of counts.10 It sensibly rounds them, so the sum of all percentages is guaranteed to be 100.11 Exception: all counts are 0.12 >>> calculate_percentages([3, 1])13 [75, 25]14 >>> calculate_percentages([35, 1])15 [97, 3]16 >>> calculate_percentages([35, 1], digits=3)17 [972, 28]18 >>> calculate_percentages([1023, 432])19 [70, 30]20 >>> calculate_percentages([1023, 432], digits=4)21 [7031, 2969]22 >>> calculate_percentages([32, 124, 81, 45])23 [11, 44, 29, 16]24 >>> calculate_percentages([0, 0])25 [0, 0]26 >>> calculate_percentages([0])27 [0]28 '''29 total = sum(counts)30 precision = 10**digits31 if total == 0:32 return [0] * len(counts)33 whole = [precision * x // total for x in counts]34 leftover = [(precision * counts[i] % total, i) for i in range(len(counts))]35 assigned = sum(whole)36 leftover.sort()37 leftover.reverse()38 i = 039 while assigned < precision:40 if counts[leftover[i][1]] > 0:41 whole[leftover[i][1]] += 142 assigned += 143 i += 144 if i == len(counts):45 i = 046 return whole47#TODO napisi dekorator @cache_value48def get_active_vote(facebook_id, force=False):49 '''50 Gets the active vote of a particular facebook user.51 '''52 key = 'vote_{}'.format(facebook_id)53 vote = cache.get(key)54 if vote is None or force:55 try:56 vote = ActiveVote.objects.get(facebook_id=facebook_id)57 except ObjectDoesNotExist:58 vote = None59 except MultipleObjectsReturned:60 vote = tasks.fix_votes(facebook_id)61 cache.set(key, vote)62 return vote63def get_global_results(force=False):64 '''65 Gets the global results from cache.66 If there are no results, we calculate them and save to cache.67 '''68 key = 'global_results'69 result = cache.get(key)70 if result is None or force:71 query_result = ActiveVote.objects.values('vote').annotate(Count('vote'))72 result = [0] * 273 for q in query_result:74 result[q['vote']] = q['vote__count']75 cache.set(key, result)76 return result77def get_global_count(force=False):78 '''79 Gets the total number of votes casted.80 '''81 return sum(get_global_results(force))82def get_friends_results(user_id, force=False):83 '''84 Gets the results for facebook friends of the user.85 If there are no results, we calculate them and save to cache.86 '''87 key = 'friends_{}'.format(user_id)88 result = cache.get(key)89 if result is None or force:90 cursor = connection.cursor()91 cursor.execute(92 'SELECT vote, COUNT(vote) ' +93 'FROM django_facebook_facebookuser AS fb ' +94 'JOIN referendum_activevote AS v ' +95 'ON fb.facebook_id = v.facebook_id ' +96 'WHERE fb.user_id=%s ' +97 'GROUP BY vote',98 [user_id]99 )100 results_raw = cursor.fetchall()101 result = [0] * 2102 for r in results_raw:103 result[r[0]] = int(r[1])104 cache.set(key, result)105 return result106def get_full_results(user_id, force=False):107 ret = {}108 friends_results = get_friends_results(user_id, force)109 ret['friends_results'] = {110 'percentages': calculate_percentages(friends_results),111 'raw_numbers': friends_results,112 'responses': sum(friends_results),113 }114#TODO: add results by age115 global_results = get_global_results(force)116 ret['global_results'] = {117 'percentages': calculate_percentages(global_results),118 'raw_numbers': global_results,119 'responses': sum(global_results)120 }121 return ret122def get_georesults(scope, location, force=False):123 if scope != 'county' and scope != 'country':124 raise KeyError('scope value incorrect!')125 if location != 'current' and location != 'hometown':126 raise KeyError('location value incorrect!')127 # our database is stupid128 if location == 'current':129 location = 'location'130 key = 'georesults_{}_{}'.format(scope, location)131 results = cache.get(key)...
WeightCalc.py
Source:WeightCalc.py
...6 print ("Get Stronger!")7 weight = int(input('For real, enter your max weight:'))8 else:9 break10def calculate_percentages( weight , percent ):11 # This takes the weight, and determines the 90, 80, 70, 60 % values12 result = weight * (percent / 100)13 print ( "%s percent of the weight %s lbs is %s" % (percent , weight, result) )14 return result15def plate_subtract ( weight , plateWeight , plates):16 double_plateWeight = plateWeight * 217 if weight >= double_plateWeight:18 plates.append(plateWeight)19 weight = weight - double_plateWeight20 return plates , weight21def plate_count ( weight ):22 plates = []23 # subtract the bar24 weight = weight - 45...
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