diff --git a/UFCFVQ-15-M Programming Task 1.ipynb b/UFCFVQ-15-M Programming Task 1.ipynb
index 20eb2f89de19b1ac6f7bd49ed7ea7b5afafb433f..3a3c6233984578fb06678438ea4982c0666ee5a3 100644
--- a/UFCFVQ-15-M Programming Task 1.ipynb
+++ b/UFCFVQ-15-M Programming Task 1.ipynb
@@ -14,9 +14,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "#### Reading the Task1.dat dataset\n",
- "\n",
- "Creating a variable to read task 1.dat to call it faster between tasks."
+ "### Requirement FR1.1 - Find the arithmetic mean"
]
},
{
@@ -28,53 +26,21 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "1) The data at this point is <class 'str'>\n",
- "2) The items inside <class 'list'> is <class 'str'> | Now its a list of string\n",
- "3) The items inside <class 'list'> is <class 'int'> | Now its a list of integer\n"
+ "The mean of the dataset is 120.89453125\n"
]
}
],
"source": [
"#Creating a variable called (List) and opening the 'task 1.dat' file as 'read' mode\n",
- "List = open('task1.dat','r')\n",
+ "TaskoneDat = open('task1.dat','r')\n",
"#Reading the data of the file.\n",
- "List = List.read()\n",
- "\n",
- "#The type is (string) at this point and item are one below each other (not a list)\n",
- "print(f'1) The data at this point is {type(List)}') \n",
- "\n",
+ "TaskoneDat = TaskoneDat.read()\n",
"#Removing the \\n and making it as list\n",
- "ConvertToList = list(List.strip().split())\n",
- "#The type is (list) at this point but each item is (string) inside the list is e.g['148', '85', '183']\n",
- "print(f'2) The items inside {type(ConvertToList)} is {type(ConvertToList[0])} | Now its a list of string')\n",
- "\n",
+ "ConvertToList = list(TaskoneDat.strip().split())\n",
"#Converting all string numbers to (integer)\n",
- "List = list(map(int, ConvertToList))\n",
- "#Now all numbers are integer niside the list e.g [148, 85, 183]\n",
- "print(f'3) The items inside {type(List)} is {type(List[0])} | Now its a list of integer')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Requirement FR1.1 - Find the arithmetic mean"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The mean of the datan list is 120.89453125\n"
- ]
- }
- ],
- "source": [
+ "TaskoneDat = list(map(int, ConvertToList))\n",
+ "\n",
+ "\n",
"#function to get the mean of a given python 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",
@@ -87,7 +53,7 @@
" return ListMean\n",
"\n",
"#Calling the function and passing the >> List << argument to it \n",
- "print(f'The mean of the datan list is {meanFinder(List)}')"
+ "print(f'The mean of the dataset is {meanFinder(TaskoneDat)}')"
]
},
{
@@ -107,14 +73,14 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Standard Devision of the data list is 31.95179590820272\n"
+ "Standard Devision of the dataset is 31.95179590820272\n"
]
}
],
@@ -130,7 +96,7 @@
" return STD_DEV\n",
"\n",
"#Calling the function and passing the >> List << argument to it \n",
- "print(f'Standard Devision of the data list is {StdDevFinder(List)}')"
+ "print(f'Standard Devision of the dataset is {StdDevFinder(TaskoneDat)}')"
]
},
{
@@ -150,15 +116,15 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "The minimum number in the data list is 0\n",
- "The maximum in the data list is 199\n"
+ "The minimum number in the dataset is 0\n",
+ "The maximum number in the dataset is 199\n"
]
}
],
@@ -176,9 +142,9 @@
"\n",
" \n",
"#Calling the functions and passing the >> List << argument to it \n",
- "print(f'The minimum number in the data list is {MinFinder(List)}')\n",
+ "print(f'The minimum number in the dataset is {MinFinder(TaskoneDat)}')\n",
"\n",
- "print(f'The maximum in the data list is {MaxFinder(List)}')\n"
+ "print(f'The maximum number in the dataset is {MaxFinder(TaskoneDat)}')\n"
]
},
{
@@ -199,7 +165,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -229,22 +195,22 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "The 25 percentile = 99\n"
+ "The 25 percentile in the dataset is 99\n"
]
}
],
"source": [
"#Finding the 25 percentile\n",
- "percentileOf_25 = percentileFinder(List,25)\n",
+ "percentileOf_25 = percentileFinder(TaskoneDat,25)\n",
"#Print the result\n",
- "print(f'The 25 percentile = {percentileOf_25}')"
+ "print(f'The 25 percentile in the dataset is {percentileOf_25}')"
]
},
{
@@ -264,22 +230,22 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "The 50 percentile = 117\n"
+ "The 50 percentile in the dataset is 117\n"
]
}
],
"source": [
"#Finding the 50 percentile\n",
- "percentileOf_50 = percentileFinder(List,50)\n",
+ "percentileOf_50 = percentileFinder(TaskoneDat,50)\n",
"#Print the result\n",
- "print(f'The 50 percentile = {percentileOf_50}')"
+ "print(f'The 50 percentile in the dataset is {percentileOf_50}')"
]
},
{
@@ -299,22 +265,22 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "The 75 percentile = 141\n"
+ "The 75 percentile in the dataset is 141\n"
]
}
],
"source": [
"#Finding the 75 percentile \n",
- "percentileOf_75 = percentileFinder(List,75)\n",
+ "percentileOf_75 = percentileFinder(TaskoneDat,75)\n",
"#Print the result\n",
- "print(f'The 75 percentile = {percentileOf_75}')"
+ "print(f'The 75 percentile in the dataset is {percentileOf_75}')"
]
},
{
@@ -334,7 +300,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -345,11 +311,11 @@
"\n",
"Count 768\n",
"Mean 120.895\n",
- "StdDev 31.952\n",
+ "Std 31.952\n",
"Minimum 0\n",
- "25 percentile 99\n",
- "50 percentile 117\n",
- "70 percentile 141\n",
+ "25% 99\n",
+ "50% 117\n",
+ "70% 141\n",
"Maximum 199\n"
]
}
@@ -357,14 +323,14 @@
"source": [
"#Creating a dictionary that contains all the needed summary with the right definition and variable\n",
"TabularDic = {\n",
- " 'Count':len(List),\n",
+ " 'Count':len(TaskoneDat),\n",
" 'Mean':round(ListMean,3),\n",
- " 'StdDev':round(StdDevFinder(List),3),\n",
- " 'Minimum':min(List),\n",
- " '25 percentile':percentileOf_25,\n",
- " '50 percentile':percentileOf_50,\n",
- " '70 percentile':percentileOf_75,\n",
- " 'Maximum':max(List)\n",
+ " 'Std':round(StdDevFinder(TaskoneDat),3),\n",
+ " 'Minimum':min(TaskoneDat),\n",
+ " '25%':percentileOf_25,\n",
+ " '50%':percentileOf_50,\n",
+ " '70%':percentileOf_75,\n",
+ " 'Maximum':max(TaskoneDat)\n",
"}\n",
"#Printing out the result\n",
"print('Summary Statistics\\n')\n",
@@ -390,32 +356,43 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "[+] The data type is <class 'list'> and the numbers inside is <class 'int'>\n",
+ "[+] The data at this point is <class 'str'>\n",
+ "[+] The items inside <class 'list'> is <class 'str'> | Now its a list of string\n",
+ "[+] The items inside <class 'list'> is <class 'int'> | Now its a list of integer\n",
"\n",
- "[+] The file content as shown:\n",
+ "[+] The data inside task1.dat is as shown\n",
"\n",
"[148, 85, 183, 89, 137, 116, 78, 115, 197, 125, 110, 168, 139, 189, 166, 100, 118, 107, 103, 115, 126, 99, 196, 119, 143, 125, 147, 97, 145, 117, 109, 158, 88, 92, 122, 103, 138, 102, 90, 111, 180, 133, 106, 171, 159, 180, 146, 71, 103, 105, 103, 101, 88, 176, 150, 73, 187, 100, 146, 105, 84, 133, 44, 141, 114, 99, 109, 109, 95, 146, 100, 139, 126, 129, 79, 0, 62, 95, 131, 112, 113, 74, 83, 101, 137, 110, 106, 100, 136, 107, 80, 123, 81, 134, 142, 144, 92, 71, 93, 122, 163, 151, 125, 81, 85, 126, 96, 144, 83, 95, 171, 155, 89, 76, 160, 146, 124, 78, 97, 99, 162, 111, 107, 132, 113, 88, 120, 118, 117, 105, 173, 122, 170, 84, 96, 125, 100, 93, 129, 105, 128, 106, 108, 108, 154, 102, 57, 106, 147, 90, 136, 114, 156, 153, 188, 152, 99, 109, 88, 163, 151, 102, 114, 100, 131, 104, 148, 120, 110, 111, 102, 134, 87, 79, 75, 179, 85, 129, 143, 130, 87, 119, 0, 73, 141, 194, 181, 128, 109, 139, 111, 123, 159, 135, 85, 158, 105, 107, 109, 148, 113, 138, 108, 99, 103, 111, 196, 162, 96, 184, 81, 147, 179, 140, 112, 151, 109, 125, 85, 112, 177, 158, 119, 142, 100, 87, 101, 162, 197, 117, 142, 134, 79, 122, 74, 171, 181, 179, 164, 104, 91, 91, 139, 119, 146, 184, 122, 165, 124, 111, 106, 129, 90, 86, 92, 113, 111, 114, 193, 155, 191, 141, 95, 142, 123, 96, 138, 128, 102, 146, 101, 108, 122, 71, 106, 100, 106, 104, 114, 108, 146, 129, 133, 161, 108, 136, 155, 119, 96, 108, 78, 107, 128, 128, 161, 151, 146, 126, 100, 112, 167, 144, 77, 115, 150, 120, 161, 137, 128, 124, 80, 106, 155, 113, 109, 112, 99, 182, 115, 194, 129, 112, 124, 152, 112, 157, 122, 179, 102, 105, 118, 87, 180, 106, 95, 165, 117, 115, 152, 178, 130, 95, 0, 122, 95, 126, 139, 116, 99, 0, 92, 137, 61, 90, 90, 165, 125, 129, 88, 196, 189, 158, 103, 146, 147, 99, 124, 101, 81, 133, 173, 118, 84, 105, 122, 140, 98, 87, 156, 93, 107, 105, 109, 90, 125, 119, 116, 105, 144, 100, 100, 166, 131, 116, 158, 127, 96, 131, 82, 193, 95, 137, 136, 72, 168, 123, 115, 101, 197, 172, 102, 112, 143, 143, 138, 173, 97, 144, 83, 129, 119, 94, 102, 115, 151, 184, 94, 181, 135, 95, 99, 89, 80, 139, 90, 141, 140, 147, 97, 107, 189, 83, 117, 108, 117, 180, 100, 95, 104, 120, 82, 134, 91, 119, 100, 175, 135, 86, 148, 134, 120, 71, 74, 88, 115, 124, 74, 97, 120, 154, 144, 137, 119, 136, 114, 137, 105, 114, 126, 132, 158, 123, 85, 84, 145, 135, 139, 173, 99, 194, 83, 89, 99, 125, 80, 166, 110, 81, 195, 154, 117, 84, 0, 94, 96, 75, 180, 130, 84, 120, 84, 139, 91, 91, 99, 163, 145, 125, 76, 129, 68, 124, 114, 130, 125, 87, 97, 116, 117, 111, 122, 107, 86, 91, 77, 132, 105, 57, 127, 129, 100, 128, 90, 84, 88, 186, 187, 131, 164, 189, 116, 84, 114, 88, 84, 124, 97, 110, 103, 85, 125, 198, 87, 99, 91, 95, 99, 92, 154, 121, 78, 130, 111, 98, 143, 119, 108, 118, 133, 197, 151, 109, 121, 100, 124, 93, 143, 103, 176, 73, 111, 112, 132, 82, 123, 188, 67, 89, 173, 109, 108, 96, 124, 150, 183, 124, 181, 92, 152, 111, 106, 174, 168, 105, 138, 106, 117, 68, 112, 119, 112, 92, 183, 94, 108, 90, 125, 132, 128, 94, 114, 102, 111, 128, 92, 104, 104, 94, 97, 100, 102, 128, 147, 90, 103, 157, 167, 179, 136, 107, 91, 117, 123, 120, 106, 155, 101, 120, 127, 80, 162, 199, 167, 145, 115, 112, 145, 111, 98, 154, 165, 99, 68, 123, 91, 195, 156, 93, 121, 101, 56, 162, 95, 125, 136, 129, 130, 107, 140, 144, 107, 158, 121, 129, 90, 142, 169, 99, 127, 118, 122, 125, 168, 129, 110, 80, 115, 127, 164, 93, 158, 126, 129, 134, 102, 187, 173, 94, 108, 97, 83, 114, 149, 117, 111, 112, 116, 141, 175, 92, 130, 120, 174, 106, 105, 95, 126, 65, 99, 102, 120, 102, 109, 140, 153, 100, 147, 81, 187, 162, 136, 121, 108, 181, 154, 128, 137, 123, 106, 190, 88, 170, 89, 101, 122, 121, 126, 93]\n"
]
}
],
"source": [
- "#Opening 'task 1.dat' file as 'read' mode and reading its content\n",
- "FileName = open('task1.dat','r')\n",
- "FileName = FileName.read()\n",
- "#Removing ('\\n')\n",
- "ConvertToList = list(FileName.strip().split())\n",
- "#Converting it to integer list\n",
- "List = list(map(int, ConvertToList))\n",
- " \n",
- "print(f'[+] The data type is {type(List)} and the numbers inside is {type(List[0])}\\n')\n",
- "print(f\"[+] The file content as shown:\\n\\n{List[:]}\")\n"
+ "#Creating a variable called (List) and opening the 'task 1.dat' file as 'read' mode\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",
+ "print(f'[+] The data at this point is {type(TaskOneDat)}') \n",
+ "\n",
+ "#Removing the \\n and making it as list\n",
+ "ConvertToList = list(TaskOneDat.strip().split())\n",
+ "#The type is (TaskOneDat) at this point but each item is (string) inside the list is e.g['148', '85', '183']\n",
+ "print(f'[+] The items inside {type(ConvertToList)} is {type(ConvertToList[0])} | Now its a list of string')\n",
+ "\n",
+ "#Converting all string numbers to (integer)\n",
+ "TaskOneDat = list(map(int, ConvertToList))\n",
+ "#Now all numbers are integer niside the TaskOneDat e.g [148, 85, 183]\n",
+ "print(f'[+] The items inside {type(TaskOneDat)} is {type(TaskOneDat[0])} | Now its a list of integer\\n')\n",
+ "\n",
+ "print(f'[+] The data inside task1.dat is as shown\\n\\n{List}')"
]
},
{
@@ -433,59 +410,62 @@
"### Requirement FR1.9 - Design and build a data structure to store CSV data in memory"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "As per as requested the data is stored in the memory and can be called later for analyzing\n"
- ]
- },
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 10,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'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": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "CsvFile = open('task1.csv','r')\n",
- "AppendTolist = [] #Creating an empty list to append all the CSV data inside it\n",
- "ColumnHeader = [] #Creating an empty list to append the CSV Column Header (Name of each column)\n",
- "\n",
- "#Refer to Explanation (1) at the end\n",
- "EachClmToList = [] #Creating an empty list to append Each column-row (each data under each line for the header ) \n",
+ "###The result are outputted based on appendix one example\n",
+ "###For better visualization , only 5 results in each column is outputted\n",
+ "##\n",
"\n",
- "'''\n",
- "For loop to read the data inside the CSV file and remove all (\\n). therefore all data will be on the same line\n",
- "but split by a (,) so each line is a sublist, eg. [[Row 1],[Row 2],[Row 3]..]\n",
- "'''\n",
- "for data in CsvFile: \n",
- " data = data.replace('\\n','').split(',')\n",
- " AppendTolist.append(data) #Append (adding) the new data into the 'AppendTolist' list\n",
- "#print(AppendTolist[0:5])\n",
+ "#Reading the CSV File\n",
+ "CSVFile = 'task1.csv'\n",
+ "CSVFileTask1 = open(CSVFile,'r')\n",
+ "EachLine = CSVFileTask1.readlines()\n",
"\n",
- "#For loop to append only the first list >> [0] << of the 'AppendTolist', which is the header row (names).\n",
- "for subList in AppendTolist[0]: \n",
- " ColumnHeader.append(subList) #append to another list. \n",
- "#Removing the names row from the main list 'AppendTolist'\n",
- "AppendTolist.remove(AppendTolist[0])\n",
+ "# Each header name will be saved inside this variable \n",
+ "CloumnName = EachLine[0].strip(\"\\n\").split(',')\n",
"\n",
- "#Refer to Explanation (2) at the end\n",
- "for clmLen in range(len(ColumnHeader)): #9 aka 8 as it starts from 0\n",
- " #print(ColumnHeader)\n",
- " for RowLen in range(len(AppendTolist)):#768 aka 767 as it satrts from 0 \n",
- " #print(AppendTolist[RowLen][clmLen])\n",
- " EachClmToList.append(AppendTolist[RowLen][clmLen]) \n",
- "#print(EachClmToList)\n",
+ "# Creating one empty dictionary and one empty list \n",
+ "Result = {}\n",
+ "ListOfData = []\n",
"\n",
- "#to avoid python errors, a new variable is created called 'numberOfDataInEach' that carries row length in each line.\n",
- "numberOfDataInEach = len(AppendTolist)\n",
+ "#Explanation 1 and 2 at the end\n",
+ "for x in range(len(CloumnName)):\n",
+ " AllValues = [subList.strip(\"\\n\").split(',')[x] for subList in EachLine] \n",
+ " test = AllValues[1:]\n",
+ " ListOfData.append(test)\n",
+ "#passing the key and value to a variable\n",
+ "for columnNumber in range(len(CloumnName)):\n",
+ " keyNames = CloumnName[columnNumber]\n",
+ " Result[keyNames] = ListOfData[columnNumber][:5] #In case to print all result remove the (5)\n",
"\n",
- "#Refer to Explanation (3) at the end\n",
- "EachClmToList = [EachClmToList[x:x+numberOfDataInEach] for x in range(0, len(EachClmToList), numberOfDataInEach)]\n",
"\n",
- "\n",
- "\n",
- "# for proven of successful output, the data can be printed out by uncommenting the next line\n",
- "#print(ColumnHeader,EachClmToList[:][:])"
+ "##For better visualization , only 5 results in each column is outputted\n",
+ "#Print out the result\n",
+ "Result\n",
+ "\n"
]
},
{
@@ -503,43 +483,6961 @@
"### Requirement FR1.10 - Create and demonstrate a function to read CSV data from a file into memory"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "- Following the given instruction in the appendix.A (output structure), this function has been created.\n",
- "- To be visualised better, the data output has been limited to 13 in each row."
- ]
- },
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\"Pregnancies\" : [6.0, 1.0, 8.0, 1.0, 0.0, 5.0, 3.0, 10.0, 2.0, 8.0, 4.0, 10.0, 10.0]\n",
- "\"Glucose\" : [148.0, 85.0, 183.0, 89.0, 137.0, 116.0, 78.0, 115.0, 197.0, 125.0, 110.0, 168.0, 139.0]\n",
- "\"BloodPressure\" : [72.0, 66.0, 64.0, 66.0, 40.0, 74.0, 50.0, 0.0, 70.0, 96.0, 92.0, 74.0, 80.0]\n",
- "\"SkinThickness\" : [35.0, 29.0, 0.0, 23.0, 35.0, 0.0, 32.0, 0.0, 45.0, 0.0, 0.0, 0.0, 0.0]\n",
- "\"Insulin\" : [0.0, 0.0, 0.0, 94.0, 168.0, 0.0, 88.0, 0.0, 543.0, 0.0, 0.0, 0.0, 0.0]\n",
- "\"BMI\" : [33.6, 26.6, 23.3, 28.1, 43.1, 25.6, 31.0, 35.3, 30.5, 0.0, 37.6, 38.0, 27.1]\n",
- "\"DiabetesPedigreeFunction\" : [0.627, 0.351, 0.672, 0.167, 2.288, 0.201, 0.248, 0.134, 0.158, 0.232, 0.191, 0.537, 1.441]\n",
- "\"Age\" : [50.0, 31.0, 32.0, 21.0, 33.0, 30.0, 26.0, 29.0, 53.0, 54.0, 30.0, 34.0, 57.0]\n",
- "\"Outcome\" : [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0]\n"
- ]
+ "data": {
+ "text/plain": [
+ "{'Pregnancies': ['6',\n",
+ " '1',\n",
+ " '8',\n",
+ " '1',\n",
+ " '0',\n",
+ " '5',\n",
+ " '3',\n",
+ " '10',\n",
+ " '2',\n",
+ " '8',\n",
+ " '4',\n",
+ " '10',\n",
+ " '10',\n",
+ " '1',\n",
+ " '5',\n",
+ " '7',\n",
+ " '0',\n",
+ " '7',\n",
+ " '1',\n",
+ " '1',\n",
+ " '3',\n",
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+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
"source": [
- "#Creating function to construct the data structure and to call it after. \n",
- "def DataDemonstration():\n",
- " for x in range(len(ColumnHeader)):\n",
- " print(f'\"{ColumnHeader[x]}\" : {list(map(float,EachClmToList[x][:13]))}') #numbers has been converted to float\n",
- " #Only 13 data have been printed to better visualize the result, for the whole result remove [:13], which will be\n",
- " #list(map(float,EachClmToList[x]))\n",
- "DataDemonstration()"
+ "###Following the given instruction in the appendix.A (output structure), this function has been created.\n",
+ "##\n",
+ "\n",
+ "#This function reads a single column from a csv file. \n",
+ "def read_csv_column(CSVFile):\n",
+ " # Creating one empty dictionary and one empty list \n",
+ " Result = {}\n",
+ " ListOfData = []\n",
+ " # Read the CSV file\n",
+ " CSVFileTask1 = open(CSVFile,'r')\n",
+ " # Read The CSV First Lines\n",
+ " EachLine = CSVFileTask1.readlines()\n",
+ " # Each header name will be saved inside this variable \n",
+ " CloumnName = EachLine[0].strip(\"\\n\").split(',')\n",
+ " #Explanation 1 and 2\n",
+ " for x in range(len(CloumnName)):\n",
+ " AllValues = [subList.strip(\"\\n\").split(',')[x] for subList in EachLine] \n",
+ " test = AllValues[1:]\n",
+ " ListOfData.append(test)\n",
+ "\n",
+ " for columnNumber in range(len(CloumnName)):\n",
+ " keyNames = CloumnName[columnNumber]\n",
+ " Result[keyNames] = ListOfData[columnNumber]\n",
+ " return Result\n",
+ "\n",
+ "SavedInMemory = read_csv_column(CSVFile)\n",
+ "SavedInMemory"
]
},
{
@@ -559,62 +7457,67 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\"Stats\" : [\"Mean\",\"Stdev\", \"Min\", Max\",\"25%\",\"50%\",\"75%\"]\n",
- "\n",
- "\"Pregnancies\" : [3.845, 3.367, 0.0, 17.0, 1.0, 3.0, 6.0]\n",
- "\"Glucose\" : [120.895, 31.952, 0.0, 199.0, 99.0, 117.0, 141.0]\n",
- "\"BloodPressure\" : [69.105, 19.343, 0.0, 122.0, 62.0, 72.0, 80.0]\n",
- "\"SkinThickness\" : [20.536, 15.942, 0.0, 99.0, 0.0, 23.0, 32.0]\n",
- "\"Insulin\" : [79.799, 115.169, 0.0, 846.0, 0.0, 32.0, 128.0]\n",
- "\"BMI\" : [31.993, 7.879, 0.0, 67.1, 27.3, 32.0, 36.6]\n",
- "\"DiabetesPedigreeFunction\" : [0.472, 0.331, 0.078, 2.42, 0.244, 0.374, 0.627]\n",
- "\"Age\" : [33.241, 11.753, 21.0, 81.0, 24.0, 29.0, 41.0]\n",
- "\"Outcome\" : [0.349, 0.477, 0.0, 1.0, 0.0, 0.0, 1.0]\n"
- ]
+ "data": {
+ "text/plain": [
+ "{'stats': ['Mean', 'stdev', 'Min', 'Max', '25th', '50th', '75th'],\n",
+ " 'Pregnancies': [3.845, 3.367, 0.0, 17.0, 1.0, 3.0, 6.0],\n",
+ " 'Glucose': [120.895, 31.952, 0.0, 199.0, 99.0, 117.0, 141.0],\n",
+ " 'BloodPressure': [69.105, 19.343, 0.0, 122.0, 62.0, 72.0, 80.0],\n",
+ " 'SkinThickness': [20.536, 15.942, 0.0, 99.0, 0.0, 23.0, 32.0],\n",
+ " 'Insulin': [79.799, 115.169, 0.0, 846.0, 0.0, 32.0, 128.0],\n",
+ " 'BMI': [31.993, 7.879, 0.0, 67.1, 27.3, 32.0, 36.6],\n",
+ " 'DiabetesPedigreeFunction': [0.472, 0.331, 0.078, 2.42, 0.244, 0.374, 0.627],\n",
+ " 'Age': [33.241, 11.753, 21.0, 81.0, 24.0, 29.0, 41.0],\n",
+ " 'Outcome': [0.349, 0.477, 0.0, 1.0, 0.0, 0.0, 1.0]}"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
}
],
"source": [
- "print('\"Stats\" : [\"Mean\",\"Stdev\", \"Min\", Max\",\"25%\",\"50%\",\"75%\"]\\n')\n",
- "\n",
- "#function for Data Statistical\n",
- "def DataStatistical():\n",
- "\n",
- " #Creating an empty list to append Each column-row (each data under each line for the header ) \n",
- " ConvertdeToFloat = []\n",
- "\n",
- " #For loop to convet rto float in a way that it can be called later in other tasks.\n",
- " for x in range(len(ColumnHeader)):\n",
- " EachClmInListToFloat = list(map(float,EachClmToList[x][:]))\n",
- " ConvertdeToFloat.append(EachClmInListToFloat)\n",
- "\n",
- " #Creating variables that contains the required information to create the datas tatistical structure\n",
+ "#function to create a statistics table\n",
+ "def statisticsTable(items):\n",
+ " #The key names\n",
+ " stats = {'stats': ['Mean', 'stdev', 'Min', 'Max', '25th', '50th', '75th']}\n",
" \n",
- " for x in range(len(ColumnHeader)):\n",
- " data = [round(meanFinder(ConvertdeToFloat[x][:]),3),\n",
- " round(StdDevFinder((ConvertdeToFloat[x][:])),3),\n",
- " MinFinder(ConvertdeToFloat[x][:]),\n",
- " MaxFinder(ConvertdeToFloat[x][:]),\n",
- " percentileFinder(ConvertdeToFloat[x][:],25), \n",
- " percentileFinder(ConvertdeToFloat[x][:],50),\n",
- " percentileFinder(ConvertdeToFloat[x][:],75)\n",
- " ]\n",
- " print(f'\"{ColumnHeader[x]}\" : {data}')\n",
- " \n",
- "DataStatistical()"
+ " #For loop to sort the items (key,value) \n",
+ " for name, data in items.items():\n",
+ " # values contain string entries so parse them to the appropriate types\n",
+ " data = [ \n",
+ " float(EachItem) if (len(EachItem.split('.'))) \n",
+ " else int(EachItem) if EachItem.isnumeric()\n",
+ " else 0 for EachItem in data\n",
+ " ]\n",
+ " \n",
+ " #To get the result, stats have the [key] value and apply each function over ecah line of data\n",
+ " stats[name] = [\n",
+ " round(meanFinder(data),3),\n",
+ " round(StdDevFinder(data),3),\n",
+ " MinFinder(data),MaxFinder(data),\n",
+ " percentileFinder(data,25),\n",
+ " percentileFinder(data,50),\n",
+ " percentileFinder(data,75)\n",
+ " ]\n",
+ " #return the result\n",
+ " return stats\n",
+ "\n",
+ "#save the result to the memory\n",
+ "statisticsToMemory = statisticsTable(SavedInMemory)\n",
+ "#Print out the result\n",
+ "statisticsToMemory"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "# MARK: %\n",
+ "##### MARK: %\n",
"#### FEEDBACK: "
]
},
@@ -627,75 +7530,72 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "*************************************************************************************************************************\n",
- " Status Mean Stdev Min Max 25% 50% 75% \n",
- "*************************************************************************************************************************\n",
- " Pregnancies 3.845 3.367 17.0 0.0 1.0 3.0 6.0\n",
- " Glucose 120.895 31.952 199.0 0.0 99.0 117.0 141.0\n",
- " BloodPressure 69.105 19.343 122.0 0.0 62.0 72.0 80.0\n",
- " SkinThickness 20.536 15.942 99.0 0.0 0.0 23.0 32.0\n",
- " Insulin 79.799 115.169 846.0 0.0 0.0 32.0 128.0\n",
- " BMI 31.993 7.879 67.1 0.0 27.3 32.0 36.6\n",
- " DiabPedFnc 0.472 0.331 2.42 0.078 0.244 0.374 0.627\n",
- " Age 33.241 11.753 81.0 21.0 24.0 29.0 41.0\n",
- " Outcome 0.349 0.477 1.0 0.0 0.0 0.0 1.0\n",
- "*************************************************************************************************************************\n"
+ " *****************************************************************************************************************\n",
+ " stats *Pregnancies *Glucose *BloodPressure *SkinThickness *Insulin * BMI * DiabetesPedigreeFunction* Age *Outcome *\n",
+ "************************************************************************************************************************\n",
+ "* Mean * 3.845 *120.895 * 69.105 * 20.536 * 79.799 * 31.993* 0.472 * 33.241* 0.349 *\n",
+ "*stdev * 3.367 * 31.952 * 19.343 * 15.942 *115.169 * 7.879 * 0.331 * 11.753* 0.477 *\n",
+ "* Min * 0.0 * 0.0 * 0.0 * 0.0 * 0.0 * 0.0 * 0.078 * 21.0 * 0.0 *\n",
+ "* Max * 17.0 * 199.0 * 122.0 * 99.0 * 846.0 * 67.1 * 2.42 * 81.0 * 1.0 *\n",
+ "* 25th * 1.0 * 99.0 * 62.0 * 0.0 * 0.0 * 27.3 * 0.244 * 24.0 * 0.0 *\n",
+ "* 50th * 3.0 * 117.0 * 72.0 * 23.0 * 32.0 * 32.0 * 0.374 * 29.0 * 0.0 *\n",
+ "* 75th * 6.0 * 141.0 * 80.0 * 32.0 * 128.0 * 36.6 * 0.627 * 41.0 * 1.0 *\n",
+ "************************************************************************************************************************\n"
]
}
],
"source": [
- "#Function to print out a custom statistical table\n",
- "def DataCustomStatistical():\n",
+ "def CustomStatisticsTable(stats, padding=1, separate='*'):\n",
+ " # All data that is saved inside (statisticsToMemory) will be appended to a list \n",
+ " AllData = []\n",
" \n",
- " #DiabetesPedigreeFunction name is changed to DiabPedFnc as it takes a lot of space to print it out.\n",
- " newList = [element.replace('DiabetesPedigreeFunction', 'DiabPedFnc') for element in ColumnHeader]\n",
- " clmName = [newList]\n",
+ " #For loop to save the data in (statistics Memory) in order to be read by this function correctly.\n",
+ " for names, data in stats.items():\n",
+ " InOrder = [names] + data \n",
+ " AllData.append(InOrder)\n",
+ " \n",
+ " # Table Data list \n",
+ " TableData = [padding + max(len(str(EachItem)) \n",
+ " for EachItem in ItemInCol) \n",
+ " for ItemInCol in AllData]\n",
" \n",
- " Status = [\"Mean\",\"Stdev\", \"Min\", \"Max\",\"25%\",\"50%\",\"75%\"]\n",
- " format_row = \"{: >13} \" * (len(Status)+1)\n",
- " print(stars.rjust(121,'*')) #Printing the stars\n",
- " print(format_row.format(\"Status\", *Status))\n",
- " print(stars.rjust(121,'*'))\n",
- "\n",
- " ConvertdeToFloat = []\n",
+ " #The upper border (*) ,which starts from a diffrent padding location\n",
+ " UpperBorder = \" \"*(TableData[0]+1) + separate.join(separate*TableData[star] \n",
+ " for star in range(1,len(TableData))) + separate*2\n",
" \n",
- " #For loop to convet into float in a way that it can be called later in other tasks.\n",
- " Status = [\"Mean\",\"Stdev\", \"Min\", \"Max\",\"25%\",\"50%\",\"75%\"]\n",
- "\n",
- " for x in range(len(ColumnHeader)): \n",
- " EachClmInListToFloat = list(map(float,EachClmToList[x][:]))\n",
- " ConvertdeToFloat.append(EachClmInListToFloat)\n",
- " \n",
- " #Creating variables that contains the required information to create the data structure\n",
- " #and calling function that has been saved in memory before.\n",
+ " #Print the upper *, which starts from 2* padding space.\n",
+ " print(UpperBorder)\n",
" \n",
- " for x in range(len(ColumnHeader)):\n",
- " \n",
- " data = [[round(meanFinder(ConvertdeToFloat[x][:]),3),\n",
- " round(StdDevFinder((ConvertdeToFloat[x][:])),3),\n",
- " MaxFinder(ConvertdeToFloat[x][:]),\n",
- " MinFinder(ConvertdeToFloat[x][:]),\n",
- " percentileFinder(ConvertdeToFloat[x][:],25), \n",
- " percentileFinder(ConvertdeToFloat[x][:],50),\n",
- " percentileFinder(ConvertdeToFloat[x][:],75)\n",
- " ]]\n",
- "\n",
- " format_row = \"{:>15}\" * (len(Status) + 1)\n",
- " \n",
- " for names, row in zip(clmName, data): #Printing the result out formatted to have an organized custom table\n",
- " #print(team[x])\n",
- " print(format_row.format(names[x], *row))\n",
+ " # Save all data inside list that contains sublists\n",
+ " RowsToList = [[ItemInCol[i] for ItemInCol in AllData]\n",
+ " for i in range(len(AllData[0]))]\n",
+ " \n",
+ " # Drow (*), without any special width or padding\n",
+ " LowerBorder = separate.join(separate*TableData[star]\n",
+ " for star in range(len(TableData))) + separate*2\n",
+ " \n",
+ " #for loop to print the custom table\n",
+ " for RowNumber, Row in enumerate(RowsToList):\n",
+ " if RowNumber==0:\n",
+ " #Print the (*) border and the key names when it is the first row \n",
+ " print(\" \" + separate.join(str(Row[line]).center(TableData[line])for line in range(len(Row))) + separate)\n",
+ " else:\n",
+ " # To print stars between each value \n",
+ " print(separate + separate.join(str(Row[star]).center(TableData[star])for star in range(len(Row)))+ separate) \n",
+ " if RowNumber == 0:\n",
+ " #When the upper first section finished and it is still row number 1, print a border of (*)\n",
+ " print(LowerBorder) \n",
+ " print(LowerBorder) \n",
"\n",
- "stars = '*'\n",
- "DataCustomStatistical()\n",
- "print(stars.rjust(121,'*'))"
+ "#Call the function and pass (statisticsToMemory) values \n",
+ "CustomStatisticsTable(statisticsToMemory)"
]
},
{
@@ -721,11 +7621,13 @@
"\n",
"#### From task 1.1 to 1.7\n",
"\n",
- "- Overall the questions can be solved using mathematical equations using pre-build function in python.\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",
"\n",
"#### From task 1.9 and along\n",
"##### Explanation 1 \n",
- "- Normal data structure is as shown \n",
+ "- The normal data structure example is as shown \n",
"\n",
" ['A','B','C'],\n",
"\n",
@@ -736,33 +7638,32 @@
" so on..\n",
" \n",
"\n",
- "- Therfore variable EachClmToList[0] = ['A','1','4'..]\n",
+ "- Therefore variable ListOfData will result to have the data in this shape = [['A','1','4'..],[['B','2','5'..]]..]\n",
"\n",
"##### Explanation 2\n",
- "- For loop is created to reach out to every single data inside 'AppendTolist' in the range of the data length, (768) or (767) if we count from 0. To be able to do so, 2 for loop should be created as shown, one to count the number of columns which is (9) or 8 if we count the zero, and the other to count the number of data in each row. 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 (numbers) will be appended to a new list called 'EachClmToList'\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",
"\n",
- "##### Explanation 3\n",
- "- A mathematical operation to create a sublist of (each data under each line for the header ) to achieve exploration 1, therefore for every end of a line create a new list for the next line, and at the end, we will have a total of 9 lists that \n",
- "achieved to carry each column of data into one list \n",
"\n",
"### Strength\n",
"\n",
- "- Some of the function can be reused again in different phases\n",
- "- The tasks is 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 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",
"\n",
"### Weaknesses\n",
"\n",
- "- The limitation of library use made the tasks take longer lines to be solved, which can be limited if python libraries such as CSV, Pandas is used.\n",
+ "- The limitation of library use made the tasks take longer lines to be solved, which can be limited if python libraries such as CSV, and Pandas are used the whole prosses would be solved with shorter lines of coding and with less time to execute.\n",
"\n",
"### 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 better algorithm using better formatting.\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",
"\n",
"### Alternative\n",
"\n",
- "- Some of the mathematical equation such as STD can be solved using another equation.\n",
- "- instead of using python pre-build function such as sum and len, for loop can be used to provide the same result. \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"
]
},
{