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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