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161 changes: 124 additions & 37 deletions notebook/problems.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -30,87 +30,161 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 1,
"id": "34720ab6",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The array elements distribute normally are:\n",
" [-2.42878499 -1.34216896 -0.1872267 -1.91076622 1.61142874 -0.39538096\n",
" -0.13866769 1.31755992 1.43755063 0.3461454 -0.3022814 0.8692907\n",
" -0.23959013 1.14812635 -0.65393263 0.87660662 -0.30258082 -0.31233049\n",
" 1.52497392 0.91100354]\n"
]
}
],
"source": [
"#import libraries\n",
"\n",
"import random\n",
"import numpy as np\n",
"\n",
"# Set seed in order to get similar results\n",
"\n",
"random.seed(20)\n",
"\n",
"# create the data\n",
"data = np.random.normal(0,1,20)\n",
"\n",
"\n",
"#print results"
"#print results\n",
"print(\"The array elements distribute normally are:\\n\",data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"id": "49c55822",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The mean of the data is: 0.0914487413075153\n"
]
}
],
"source": [
"#Use numpy to get the mean of your data"
"#Use numpy to get the mean of your data\n",
"print(\"The mean of the data is: \",data.mean())"
]
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 3,
"id": "03529459",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The variation of the data is: 1.2338555658446149\n"
]
}
],
"source": [
"#get the variance of your data"
"#get the variance of your data\n",
"print(\"The variation of the data is: \",data.var())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e53f30c5",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The standard deviation of the data is: 1.1107905139334846\n"
]
}
],
"source": [
"# Standard deviation\n"
"# Standard deviation\n",
"print(\"The standard deviation of the data is: \",data.std())"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "9bce852f",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The data mode is: -2.428784987925977\n"
]
}
],
"source": [
"#import libraries and print the mode\n",
"import statistics\n",
"\n",
"\n",
"# Mode for continuous array\n"
"# Mode for continuous array\n",
"data_mode = statistics.mode(data)\n",
"print(\"The data mode is: \",data_mode)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "c682cb6e",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The data median is: -0.16294719647810535\n"
]
}
],
"source": [
"# Median\n"
"# Median\n",
"data_median = statistics.median(data)\n",
"print(\"The data median is: \",data_median)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "39c3fabd",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The Quantiles of the data are:\n",
" [-0.374618339376134, -0.16294719647810535, 1.0888456458137472]\n",
"The median data is: -0.16294719647810535\n",
"The median value match with the quantile match because, this value, represent the value tha we can find in the middle of the dataset\n"
]
}
],
"source": [
"# Print the Quantiles\n",
"print(\"The Quantiles of the data are:\\n\",statistics.quantiles(data))\n",
"\n",
"\n",
"# This match with np.median, why?\n"
"# This match with np.median, why?\n",
"print(\"The median data is: \",np.median(data))\n",
"print(\"The median value match with the quantile match because, this value, represent the value tha we can find in the middle of the dataset\")"
]
},
{
Expand All @@ -128,16 +202,17 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 11,
"id": "d590308e",
"metadata": {},
"outputs": [
{
"ename": "IndentationError",
"evalue": "expected an indented block (4006267275.py, line 20)",
"output_type": "error",
"traceback": [
"\u001b[1;36m Input \u001b[1;32mIn [8]\u001b[1;36m\u001b[0m\n\u001b[1;33m def avg_calc(ls):\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m expected an indented block\n"
"name": "stdout",
"output_type": "stream",
"text": [
"The data values are:\n",
" [4, 2, 5, 8, 6]\n",
"The Standard deviation is: 2.0\n"
]
}
],
Expand All @@ -149,11 +224,17 @@
"\n",
"def sd_calc(data):\n",
" #code here\n",
" n = len(data)\n",
" if n<= 1:\n",
" return 0.0\n",
" \n",
" \n",
" \n",
"\n",
" mean = avg_calc(data)\n",
" sd = 0\n",
" for x in data:\n",
" sd += (float(x) - mean)**2\n",
" # calculate stan. dev.\n",
" sd = math.sqrt(sd / float(n))\n",
" return sd\n",
" \n",
"\n",
"\n",
Expand All @@ -163,20 +244,26 @@
"\n",
"def avg_calc(ls):\n",
" #code here\n",
" n = len(ls)\n",
" if n <= 1:\n",
" return ls[0]\n",
" \n",
" \n",
" \n",
"\n",
" mean = 0\n",
" for x in ls:\n",
" mean += float(x) \n",
" # calculate average\n",
" mean = mean / float(n)\n",
" return mean\n",
" \n",
" \n",
" \n",
"\n",
"data = [4, 2, 5, 8, 6]\n",
"\n",
"#print the data\n",
"\n",
"#print the standard deviation of the data"
"print(\"The data values are:\\n\",data)\n",
"#print the standard deviation of the data\n",
"print(\"The Standard deviation is: \",sd_calc(data))"
]
},
{
Expand Down Expand Up @@ -209,7 +296,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.12"
"version": "3.11.4"
}
},
"nbformat": 4,
Expand Down