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# Replace null values with string "no" (i.e., meaning that employee has not left the company)
df['Departure Date'] = df['Departure Date'].fillna("no")

# Get a list of all months from the hiring date of the first employee until current month
all_months = pd.date_range(df["Hiring Date"].unique()[0], datetime.datetime.today().strftime('%m/%d/%Y'), freq='MS').strftime("%Y-%b").tolist()

# Initialize list for cummulative employees
cummulative_employees = []

# Loop through all the months in list

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# Import libraries and dependencies
import pandas as pd
import datetime
import matplotlib.pyplot as plt
%matplotlib inline

# Read employees csv file
df = pd.read_csv("employees.csv")

# Display top rows

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def max_repeated_character(word):
    
    # Validate that provided word is not an empty string
    if word != "":
    
        # Initialize variables
        repeated_character = word[0]
        character_repeated_times = 1
        max_repeated_times = 1

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# Rule 3 - Lower
for i in range(5, len(df_grouped['x_bar'])+1): 
    if((df_grouped['x_bar'][i-4] < df_grouped['-1s'][i-4] and df_grouped['x_bar'][i-3] < df_grouped['-1s'][i-3] and df_grouped['x_bar'][i-2] < df_grouped['-1s'][i-2] and df_grouped['x_bar'][i-1] < df_grouped['-1s'][i-1]) or
       (df_grouped['x_bar'][i-4] < df_grouped['-1s'][i-4] and df_grouped['x_bar'][i-3] < df_grouped['-1s'][i-3] and df_grouped['x_bar'][i-2] < df_grouped['-1s'][i-2] and df_grouped['x_bar'][i] < df_grouped['-1s'][i]) or
       (df_grouped['x_bar'][i-4] < df_grouped['-1s'][i-4] and df_grouped['x_bar'][i-2] < df_grouped['-1s'][i-2] and df_grouped['x_bar'][i-1] < df_grouped['-1s'][i-1] and df_grouped['x_bar'][i] < df_grouped['-1s'][i]) or
       (df_grouped['x_bar'][i-4] < df_grouped['-1s'][i-4] and df_grouped['x_bar'][i-3] < df_grouped['-1s'][i-3] and df_grouped['x_bar'][i-1] < df_grouped['-1s'][i-1] and df_grouped['x_bar'][i] < df_grouped['-1s'][i]) or
       (df_grouped['x_bar'][i-3] < df_grouped['-1s'][i-3] and df_grouped['x

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# Look for at least one False value in each of the control chart rules
analysis.all()

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# Define outcomes data frame
analysis = pd.DataFrame({'R1_lower':R1_lower,
                        'R1_upper':R1_upper,
                        'R2_lower':R2_lower,
                        'R2_upper':R2_upper,
                        'R3_lower':R3_lower,
                        'R3_upper':R3_upper,
                        'R4_lower':R4_lower,
                        'R4_upper':R4_upper})

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# Rule 4 - Lower
for i in range(8,len(df_grouped)+1):
    if (df_grouped["x_bar"][i-8:i] < df_grouped["x_bar_bar"][1]).all():
        R4_lower.append(False)
    else:
        R4_lower.append(True)
        
# Rule 4 - Upper
for i in range(8,len(df_grouped)+1):
    if (df_grouped["x_bar"][i-8:i] > df_grouped["x_bar_bar"][1]).all():

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# Rule 2 - Lower
for i in range(2,len(df_grouped)+1):
    if df_grouped["x_bar"][i] < df_grouped["-2s"][i] and df_grouped["x_bar"][i-1] < df_grouped["-2s"][i-1]:
        R2_lower.append(False)
    else:
        R2_lower.append(True)
        
# Rule 2 - Upper
for i in range(2,len(df_grouped)+1):
    if df_grouped["x_bar"][i] > df_grouped["+2s"][i] and df_grouped["x_bar"][i-1] > df_grouped["+2s"][i-1]:

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# Rule 1 - Lower
for x in df_grouped['x_bar']:
    if x < df_grouped['LCL'][1]:
        R1_lower.append(False)
    else:
        R1_lower.append(True)
        
# Rule 1 - Upper
for x in df_grouped['x_bar']:
    if x > df_grouped['UCL'][1]:

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# Control chart rules lists setup
R1_lower = []
R1_upper = []
R2_lower = ['-']
R2_upper = ['-']
R3_lower = ['-','-','-','-']
R3_upper = ['-','-','-','-']
R4_lower = ['-','-','-','-','-','-','-']
R4_upper = ['-','-','-','-','-','-','-']