diff --git a/UFCFVQ-15-M Programming Task 1.ipynb b/UFCFVQ-15-M Programming Task 1.ipynb
index be6cb216b9e57dcac71dc60cc20a68cde0a0ad63..7d4af61e244015f0d70094df61aea3ad87ac7dda 100644
--- a/UFCFVQ-15-M Programming Task 1.ipynb
+++ b/UFCFVQ-15-M Programming Task 1.ipynb
@@ -31,28 +31,28 @@
}
],
"source": [
- "#Creating a variable called (List) and opening the 'task 1.dat' file as 'read' mode\n",
+ "#Create a variable called 'TaskoneDat' and open the 'task1.dat' file in 'read' mode\n",
"TaskoneDat = open('task1.dat','r')\n",
- "#Reading the data of the file.\n",
+ "#Read the data from the file\n",
"TaskoneDat = TaskoneDat.read()\n",
- "#Removing the \\n and making it as list\n",
+ "#Remove newline characters and create a list from the data\n",
"ConvertToList = list(TaskoneDat.strip().split())\n",
- "#Converting all string numbers to (integer)\n",
+ "#Convert all elements in the list to integers\n",
"TaskoneDat = list(map(int, ConvertToList))\n",
"\n",
"\n",
- "#function to get the mean of a given python list\n",
+ "#Define a function to find the mean of a given list\n",
"def meanFinder(List):\n",
- " #assining the variable to be global so it can be called any were else (as it has been called in FR1.7)\n",
+ " # Assign the global variable 'ListMean' (as it has been called in FR1.7)\n",
" global ListMean\n",
"\n",
- " #Find the mean of the list using the mathematical mean equation\n",
+ " #Calculate the mean of the list using the mathematical mean equation\n",
" ListMean = sum(List)/len(List)\n",
"\n",
- " #Rturn The result\n",
+ " #Return the result\n",
" return ListMean\n",
"\n",
- "#Calling the function and passing the >> List << argument to it \n",
+ "# Call the function and pass the list as an argument\n",
"print(f'The mean of the dataset is {meanFinder(TaskoneDat)}')"
]
},
@@ -85,17 +85,17 @@
}
],
"source": [
- "#Function to find the standard deviation of a given python list\n",
+ "#Define a function to find the standard deviation of a given list\n",
"def StdDevFinder(List): \n",
" #Find the variance among the list using its mathematical equation\n",
" # (Square each deviation from the mean & Calculate the variance)\n",
" StdVariance = sum((DataInList - ListMean)**2 for DataInList in List) / len(List)\n",
" STD_DEV = StdVariance ** 0.5\n",
" \n",
- " #Rturn The result\n",
+ " #Return the result\n",
" return STD_DEV\n",
"\n",
- "#Calling the function and passing the >> List << argument to it \n",
+ "#Call the function and pass the list as an argument\n",
"print(f'Standard Devision of the dataset is {StdDevFinder(TaskoneDat)}')"
]
},
@@ -129,19 +129,21 @@
}
],
"source": [
- "#Function to find the minimum value in a python list\n",
+ "#Define a function to find the minimum value in a list\n",
"def MinFinder(List):\n",
+ " # Find the minimum value in the list\n",
" ListMin = min(List)\n",
" return ListMin\n",
"\n",
"\n",
- "#Function to find the maximum value in a python list\n",
+ "#Define a function to find the maximum value in a list\n",
"def MaxFinder(List):\n",
+ " # Find the maximum value in the list\n",
" ListMax = max(List)\n",
" return ListMax\n",
"\n",
" \n",
- "#Calling the functions and passing the >> List << argument to it \n",
+ "#Call the functions and pass the list as an argument\n",
"print(f'The minimum number in the dataset is {MinFinder(TaskoneDat)}')\n",
"\n",
"print(f'The maximum number in the dataset is {MaxFinder(TaskoneDat)}')\n"
@@ -169,14 +171,15 @@
"metadata": {},
"outputs": [],
"source": [
- "#A function has been created before the question to calculate all percentile inside the List\n",
- "#The function takes two arguments, List(the data list), percentile(the percentile number)\n",
+ "#A function has been created to calculate percentiles within a list\n",
+ "#The function takes two arguments: 1) data- the list of data. 2) percentile - the percentile number to calculate\n",
"def percentileFinder(List,percentile):\n",
- " #Firstly, finding the length of the list\n",
+ " # Find the length of the list\n",
" ListNumber = len(List)\n",
- " #Multiplying the length with the percentile number and dividing it by 100 (percentile equation) \n",
+ " # Calculate the desired percentile using the equation:\n",
+ " # (length of list * percentile) / 100\n",
" Listpercentile = ListNumber * percentile / 100\n",
- " #If the number inside the (ListPercentile) is integer, sort it and return it to (List Percentile list)\n",
+ " # If the calculated index is an integer, sort the data and return the value at that index\n",
" if Listpercentile.is_integer():\n",
" Listpercentile = sorted(List)[int(Listpercentile)]\n",
" return Listpercentile\n",
@@ -207,7 +210,7 @@
}
],
"source": [
- "#Finding the 25 percentile\n",
+ "#Calculate the 25th percentile of the data\n",
"percentileOf_25 = percentileFinder(TaskoneDat,25)\n",
"#Print the result\n",
"print(f'The 25 percentile in the dataset is {percentileOf_25}')"
@@ -242,7 +245,7 @@
}
],
"source": [
- "#Finding the 50 percentile\n",
+ "#Calculate the 50th percentile of the data\n",
"percentileOf_50 = percentileFinder(TaskoneDat,50)\n",
"#Print the result\n",
"print(f'The 50 percentile in the dataset is {percentileOf_50}')"
@@ -277,7 +280,7 @@
}
],
"source": [
- "#Finding the 75 percentile \n",
+ "#Calculate the 75th percentile of the data\n",
"percentileOf_75 = percentileFinder(TaskoneDat,75)\n",
"#Print the result\n",
"print(f'The 75 percentile in the dataset is {percentileOf_75}')"
@@ -321,7 +324,7 @@
}
],
"source": [
- "#Creating a dictionary that contains all the needed summary with the right definition and variable\n",
+ "#Create a dictionary that contains all the needed summary statistics\n",
"TabularDic = {\n",
" 'Count':len(TaskoneDat),\n",
" 'Mean':round(ListMean,3),\n",
@@ -334,7 +337,7 @@
"}\n",
"#Printing out the result\n",
"print('Summary Statistics\\n')\n",
- "#Using the for loop to access each items in the dictionary (definition = names) (variables = data)\n",
+ "#Iterate through the dictionary and print the definition and variables\n",
"for definition, variables in TabularDic.items():\n",
" print(f'{definition: <30}{variables}')"
]
@@ -374,12 +377,12 @@
}
],
"source": [
- "#Creating a variable called (List) and opening the 'task 1.dat' file as 'read' mode\n",
+ "#Open the 'task1.dat' file in 'read' mode and assign the data to the variable 'TaskOneDat'\n",
"TaskOneDat = open('task1.dat','r')\n",
"#Reading the data of the file.\n",
"TaskOneDat = TaskOneDat.read()\n",
"\n",
- "#The type is (string) at this point and item are one below each other (not a list)\n",
+ "#At this point, the data is a string\n",
"print(f'[+] The data at this point is {type(TaskOneDat)}') \n",
"\n",
"#Removing the \\n and making it as list\n",
@@ -435,8 +438,8 @@
}
],
"source": [
- "###The result are outputted based on appendix one example\n",
- "###For better visualization , only 5 results in each column is outputted\n",
+ "###The results are output based on appendix one example\n",
+ "###For better visualization, only 5 results in each column are outputted\n",
"##\n",
"\n",
"#Reading the CSV File\n",
@@ -444,10 +447,10 @@
"CSVFileTask1 = open(CSVFile,'r')\n",
"EachLine = CSVFileTask1.readlines()\n",
"\n",
- "# Each header name will be saved inside this variable \n",
+ "#Each header name will be saved inside this variable \n",
"CloumnName = EachLine[0].strip(\"\\n\").split(',')\n",
"\n",
- "# Creating one empty dictionary and one empty list \n",
+ "#Creating one empty dictionary and one empty list \n",
"Result = {}\n",
"ListOfData = []\n",
"\n",
@@ -462,7 +465,7 @@
" Result[keyNames] = ListOfData[columnNumber][:5] #In case to print all result remove the (5)\n",
"\n",
"\n",
- "##For better visualization , only 5 results in each column is outputted\n",
+ "#For better visualization, only 5 results in each column are outputted\n",
"#Print out the result\n",
"Result\n",
"\n"
@@ -485,6936 +488,33 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
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+ "{'Pregnancies': ['6', '1', '8', '1', '0'],\n",
+ " 'Glucose': ['148', '85', '183', '89', '137'],\n",
+ " 'BloodPressure': ['72', '66', '64', '66', '40'],\n",
+ " 'SkinThickness': ['35', '29', '0', '23', '35'],\n",
+ " 'Insulin': ['0', '0', '0', '94', '168'],\n",
+ " 'BMI': ['33.6', '26.6', '23.3', '28.1', '43.1'],\n",
+ " 'DiabetesPedigreeFunction': ['0.627', '0.351', '0.672', '0.167', '2.288'],\n",
+ " 'Age': ['50', '31', '32', '21', '33'],\n",
+ " 'Outcome': ['1', '0', '1', '0', '1']}"
]
},
- "execution_count": 11,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"###Following the given instruction in the appendix.A (output structure), this function has been created.\n",
- "##\n",
+ "##For better visualization, only 5 results in each column are outputted\n",
"\n",
- "#This function reads a single column from a csv file. \n",
+ "#This function reads all columns from a csv file. \n",
"def read_csv_column(CSVFile):\n",
" # Creating one empty dictionary and one empty list \n",
" Result = {}\n",
@@ -7433,9 +533,9 @@
"\n",
" for columnNumber in range(len(CloumnName)):\n",
" keyNames = CloumnName[columnNumber]\n",
- " Result[keyNames] = ListOfData[columnNumber]\n",
+ " Result[keyNames] = ListOfData[columnNumber][:5] #remove [:5] to print all result\n",
" return Result\n",
- "\n",
+ "##>>> For better visualization, only 5 results in each column are outputted <<<\n",
"SavedInMemory = read_csv_column(CSVFile)\n",
"SavedInMemory"
]
@@ -7629,53 +729,41 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "### 1)Explanation\n",
- "\n",
- "#### 1.1) From task 1.1 to 1.7\n",
+ "### 1) Explanation\n",
+ "#### From tasks 1.1 to 1.7\n",
+ "- The questions in these tasks can be solved using mathematical equations, utilizing pre-built functions in Python. These tasks are straightforward and have been solved using the task1.dot file.\n",
"\n",
- "- Overall the questions can be solved using mathematical equations using a pre-build function in python.\n",
- "- All questions are straight to the point.\n",
- "- All questions have been solved using task1.dot file\n",
+ "##### From task 1.9 and onwards\n",
+ "- Explanation 1\n",
+ " In this task, the data is structured in the following way:\n",
+ " ['A','B','C'],\n",
+ " ['1','2','3'],\n",
+ " ['4','5','6'],\n",
"\n",
- "#### 1.2) From task 1.9 and along\n",
- "##### 1.2.1) Explanation 1 \n",
- "- The normal data structure example is as shown \n",
+ " and so on...\n",
"\n",
- " ['A','B','C'],\n",
- "\n",
- " ['1','2','3'],\n",
- "\n",
- " ['4','5','6'],\n",
- " \n",
- " so on..\n",
- " \n",
+ " As a result, the variable 'ListOfData' will have data in the following shape: [['A','1','4'..],[['B','2','5'..]]..]\n",
"\n",
- "- Therefore variable ListOfData will result to have the data in this shape = [['A','1','4'..],[['B','2','5'..]]..]\n",
"\n",
- "##### 1.2.2) Explanation 2\n",
- "- For loop is created to reach out to every single data inside 'ListOfData' in the range of the data length, (768) or (767) if we count from 0. therefore it will start from [0][0].. [1][0].. [2][0] until it reaches [767][0] and switch to [0][1].. and continue until it reaches the last data in the file which is at [767][8]. while so, the data will be appended to a new list called 'ListOfData' as explained in explanation 1.\n",
+ "- Explanation 2\n",
"\n",
+ " A for loop is created to access every single data element in 'ListOfData', ranging from 0 to 768 (or 767 if we start counting from 0). The loop starts at [0][0] and continues to [767][0], then moves on to [0][1] and so on until it reaches the last data element at [767][8]. As the loop progresses, the data is appended to a new list called 'ListOfData', as explained in Explanation 1.\n",
"\n",
"### 2) Strength\n",
"\n",
- "- Some of the functions can be reused again in different phases\n",
- "- The tasks are connected all together and take results from each other without the need to re-write all from scratch\n",
+ "- Some of the functions can be reused in different phases, and the tasks are interconnected, allowing results from one task to be used in another without the need to rewrite the entire code.\n",
"\n",
"### 3) Weaknesses\n",
"\n",
- "- One weakness of the code is that it relies heavily on pre-built Python functions and does not make use of libraries such as CSV and Pandas, which could potentially make the code shorter and more efficient.\n",
+ "- One weakness of the code is that it heavily relies on pre-built Python functions and does not make use of libraries such as CSV and Pandas, which could potentially make the code shorter and more efficient.\n",
"\n",
"### 4) Improvement Points\n",
"\n",
- "- Any python program can be improved and can be written in fewer lines.\n",
- "- The custom table can be improved and created with a better algorithm using better formatting.\n",
- "- The tasks from 1.1 to 1.7 can be updated to be solved using pure python, without the use of pre-build python functions. \n",
+ "- Any Python program can be improved and written in fewer lines. The custom table can be improved and created with a more efficient algorithm and better formatting. The tasks from 1.1 to 1.7 can be updated to be solved using pure Python, without the use of pre-built functions.\n",
"\n",
"### 5) Alternative\n",
"\n",
- "- Some of the mathematical equations such as STD can be solved using another equation.\n",
- "- Instead of using python pre-build functions such as sum and len, for loop can be used to provide the same result. \n",
- "- The custom table can be created using another shape such as (+) or (dots) instead of (*)\n"
+ "- Some of the mathematical equations, such as standard deviation, can be solved using alternative equations. Instead of using pre-built functions such as 'sum' and 'len', a for loop can be used to achieve the same result. The custom table can also be created using different shapes, such as (+) or (dots) instead of (*)."
]
},
{