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lab-understanding-descriptive-stats_main.ipynb #79

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224 changes: 196 additions & 28 deletions your-code/main.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -11,11 +11,15 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Libraries"
"# Libraries\n",
"import pandas as pd\n",
"import numpy as np\n",
"import random\n",
"import matplotlib.pyplot as plt"
]
},
{
Expand All @@ -29,11 +33,18 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# your code here"
"# your code here\n",
"\n",
"def roll_dice(x):\n",
" roll = np.random.randint(1,7, size = x)\n",
" df = pd.DataFrame(roll, columns=['roll'])\n",
" return df\n",
"\n",
"df = roll_dice(10)"
]
},
{
Expand All @@ -45,11 +56,28 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# your code here"
"# your code here\n",
"df = df.sort_values(by=['roll']).reset_index(drop=True)\n",
"\n",
"plt.scatter(df.index, df['roll'])\n",
"plt.show()"
]
},
{
Expand All @@ -61,11 +89,27 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 4,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# your code here"
"# your code here\n",
"count = df['roll'].value_counts()\n",
"plt.hist(df['roll'])\n",
"plt.show()"
]
},
{
Expand All @@ -76,6 +120,7 @@
"source": [
"\"\"\"\n",
"your comments here\n",
"The scatter plot show every occurence of value\n",
"\"\"\""
]
},
Expand All @@ -91,11 +136,49 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 8,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"2.9"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# your code here"
"# your code here\n",
"def mean_rolls(x):\n",
" sum_rolls = x.sum()\n",
" times_rolls = len(x)\n",
" mean_roll = sum_rolls / times_rolls\n",
" return mean_roll\n",
"\n",
"mean_rolls(df['roll'])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2.9"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['roll'].mean()"
]
},
{
Expand All @@ -107,11 +190,34 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 10,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"{1: 1, 2: 4, 3: 1, 4: 3, 5: 1}"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# your code here"
"# your code here\n",
"\n",
"def frequency(x):\n",
"\n",
" freq = {}\n",
" for item in x:\n",
" if (item in freq):\n",
" freq[item] += 1\n",
" else:\n",
" freq[item] = 1\n",
" return freq\n",
"\n",
"frequency(df['roll'])\n"
]
},
{
Expand All @@ -124,11 +230,36 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 11,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"2.5"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# your code here"
"# your code here\n",
"\n",
"def median(list):\n",
" mylist = [x for x in list]\n",
" middle = int(len(mylist)/2)\n",
" \n",
" if len(mylist) % 2 == 0:\n",
" median = (mylist[int(middle)] + mylist[int(middle -1)]) / 2\n",
" \n",
" else:\n",
" median = mylist[middle]\n",
" \n",
" return median\n",
"\n",
"median(df['roll'])"
]
},
{
Expand All @@ -140,11 +271,32 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 12,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"0 1\n",
"1 2\n",
"2 2\n",
"3 2\n",
"4 2\n",
"5 3\n",
"6 4\n",
"7 4\n",
"8 4\n",
"9 5\n",
"Name: roll, dtype: int32"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# your code here"
"df['roll']"
]
},
{
Expand All @@ -158,11 +310,27 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 14,
"metadata": {},
"outputs": [],
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (1076281805.py, line 2)",
"output_type": "error",
"traceback": [
"\u001b[1;36m File \u001b[1;32m\"C:\\Users\\Deco\\AppData\\Local\\Temp\\ipykernel_16272\\1076281805.py\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m data2 = pd.read_csv(C:\\Users\\Deco\\Desktop\\IRONHACK\\SQL\\lab-understanding-descriptive-stats\\data\\\\roll_the_dice_hundred.csv')\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
]
}
],
"source": [
"# your code here"
"# your code here\n",
"data2 = pd.read_csv(C:\\Users\\Deco\\Desktop\\IRONHACK\\SQL\\lab-understanding-descriptive-stats\\data\\\\roll_the_dice_hundred.csv')\n",
"\n",
"df2 = pd.DataFrame(data2)\n",
"df2 = df2.sort_values(by=['value'])\n",
"\n",
"plt.scatter(df2.index, df2['value'])\n",
"plt.show()"
]
},
{
Expand Down Expand Up @@ -500,9 +668,9 @@
],
"metadata": {
"kernelspec": {
"display_name": "ironhack-3.7",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "ironhack-3.7"
"name": "python3"
},
"language_info": {
"codemirror_mode": {
Expand All @@ -514,7 +682,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.10.4"
}
},
"nbformat": 4,
Expand Down