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// Single column replace multiple characters

Function to remove multiple special characters
let
    Source = (col_val as text) => 
    let

        clean_text = if col_val = null then col_val else 
                                      List.Accumulate(Text.ToList("^{}®™")
                                      ,col_val, 

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let
    Source = Excel.Workbook(File.Contents("G:\My Drive\Eric\DynamicPivot-Two products Six Suppliers.xlsx"), null, true),
    Sheet1_Sheet = Source{[Item="Sheet1",Kind="Sheet"]}[Data],
    set_headers = Table.PromoteHeaders(Sheet1_Sheet, [PromoteAllScalars=true]),
    set_types = Table.TransformColumnTypes(set_headers,{{"Product No", Int64.Type}, {"Supplier", type text}, {"Month", type text}, {"Amount", Int64.Type}}),
    replace_null_amounts = Table.ReplaceValue(set_types,null,0,Replacer.ReplaceValue,{"Amount"}),
    group_by_product_supplier_month = Table.Group(replace_null_amounts, {"Product No", "Supplier", "Month"}, {{"Total", each List.Sum([Amount]), type nullable number}}),
    pivot_months = Table.Pivot(group_by_product_supplier_month, List.Distinct(group_by_product_supplier_month[Month]), "Month", "Total", List.Sum),
    remove_space_from_column_name = Table.RenameColumns(pivot_months,{{"Product No", "Product_No"}}),

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## Environment creation for power bi ML
conda create -n py37 -c anaconda python=3.7
conda activate py37_env
pip install matplotlib
pip install pandas
pip install sckit-learn
pip install xgboost

Set the environment
C:\Users\username\miniconda3\envs\py37_env

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# Define a helper function to fix truncated zeros, with one parameter: the value to be processed
def fix_trunc_zeros(val):
    # Use an if statement to check if there are four characters in the string representation of the value
    if len(str(val)) == 4:
        # If this is the case, return the value with an appended "0" in the front
        return "0"+str(val)
    # Otherwise...
    else:
        # Return the value itself
        return str(val)

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def biplot(score, coeff, maxdim, pcax, pcay, labels=None):
    """
      This function uses 
      score - the transformed data returned by pca - data as expressed according to the new axis
      coeff - the loadings from the pca_components_
      
      For the feaures we are interested in, it plots the correlation between the original features and the PCAs.
      Use cosine similarity and angle measures between axes.
      

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import altair as alt

#Simple Altait plot
import altair as alt
def plotGenericLineChart(x_values, x_type , y_values, y_type, chart_title, x_axis_title, y_axis_title):
    """
        This function creates a line chart for two values X and Y passed in along with thier types and titles
    """
    df = pd.DataFrame({"x_values" : x_values, 'y_values': y_values})
    chart = alt.Chart(df).mark_line(

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import altair as alt
from vega_datasets import data

# uses intermediate json files to speed things up
alt.data_transformers.enable('json')

domain_stock_list = ["AAPL", "DIS", "FB", "MSFT", "NFLX", "TSLA"]

def getBaseChart():
    """

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from os import listdir

def getAllStockData(data):
    """
        Retrieves data from files downloaded from Marketwatch and stored in a directory with path data
    
    """
    stock_df = pd.DataFrame()
    for filename in listdir("data"):
        if filename.startswith("Dow"):

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# The following code to create a dataframe and remove duplicated rows is always executed and acts as a preamble for your script:

# dataset = pandas.DataFrame(Emoji, Use Percent, Use Number)
# dataset = dataset.drop_duplicates()

# Paste or type your script code here:

# The following code to create a dataframe and remove duplicated rows is always executed and acts as a preamble for your script:

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def getAmenitiesTokens(cell_val):
    return regexp_tokenize(cell_val, "([\w\s\d\']+), ")

#Clear out unicode and unnecessary square brackets
amenities_df['amenities'] = amenities_df['amenities'].replace({'\[': '', '\]': '', '"':'', r'\\u2019': r"'", r"\\u2013":"-", r"\\u00a0":""}, regex=True)
# Follow the patttern! Lower case everything to make items similar
amenities_df['amenities'] = amenities_df['amenities'].str.lower()

#Apply the tokenizer
amenities_df["amenities_tokens"] = amenities_df["amenities"].apply(getAmenitiesTokens )