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1d334d5105a1a432e18f2e361780bab5
mooc-rr
Commits
995ee9f9
Commit
995ee9f9
authored
May 07, 2022
by
1d334d5105a1a432e18f2e361780bab5
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adding own analysis
parent
318b0415
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-26
exo5_fr.ipynb
module2/exo5/exo5_fr.ipynb
+149
-26
No files found.
module2/exo5/exo5_fr.ipynb
View file @
995ee9f9
...
@@ -261,30 +261,30 @@
...
@@ -261,30 +261,30 @@
"</div>"
"</div>"
],
],
"text/plain": [
"text/plain": [
"
Date Count Temperature Pressure Malfunction\n",
" Date Count Temperature Pressure Malfunction\n",
"0
4/12/81 6 66 50 0\n",
"0 4/12/81 6 66 50 0\n",
"1
11/12/81 6 70 50 1\n",
"1 11/12/81 6 70 50 1\n",
"2
3/22/82 6 69 50 0\n",
"2 3/22/82 6 69 50 0\n",
"3
11/11/82 6 68 50 0\n",
"3 11/11/82 6 68 50 0\n",
"4
4/04/83 6 67 50 0\n",
"4 4/04/83 6 67 50 0\n",
"5
6/18/82 6 72 50 0\n",
"5 6/18/82 6 72 50 0\n",
"6
8/30/83 6 73 100 0\n",
"6 8/30/83 6 73 100 0\n",
"7
11/28/83 6 70 100 0\n",
"7 11/28/83 6 70 100 0\n",
"8
2/03/84 6 57 200 1\n",
"8 2/03/84 6 57 200 1\n",
"9
4/06/84 6 63 200 1\n",
"9 4/06/84 6 63 200 1\n",
"10
8/30/84 6 70 200 1\n",
"10 8/30/84 6 70 200 1\n",
"11
10/05/84 6 78 200 0\n",
"11 10/05/84 6 78 200 0\n",
"12
11/08/84 6 67 200 0\n",
"12 11/08/84 6 67 200 0\n",
"13
1/24/85 6 53 200 2\n",
"13 1/24/85 6 53 200 2\n",
"14
4/12/85 6 67 200 0\n",
"14 4/12/85 6 67 200 0\n",
"15
4/29/85 6 75 200 0\n",
"15 4/29/85 6 75 200 0\n",
"16
6/17/85 6 70 200 0\n",
"16 6/17/85 6 70 200 0\n",
"17 7/29/85 6 81 200 0\n",
"17
7/29/85 6 81 200 0\n",
"18
8/27/85 6 76 200 0\n",
"18 8/27/85 6 76 200 0\n",
"19
10/03/85 6 79 200 0\n",
"19 10/03/85 6 79 200 0\n",
"20
10/30/85 6 75 200 2\n",
"20 10/30/85 6 75 200 2\n",
"21
11/26/85 6 76 200 0\n",
"21 11/26/85 6 76 200 0\n",
"22
1/12/86 6 58 200 1"
"22 1/12/86 6 58 200 1"
]
]
},
},
"execution_count": 1,
"execution_count": 1,
...
@@ -299,6 +299,130 @@
...
@@ -299,6 +299,130 @@
"data"
"data"
]
]
},
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# OWN ANALYSIS"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 False\n",
"1 True\n",
"2 False\n",
"3 False\n",
"4 False\n",
"5 False\n",
"6 False\n",
"7 False\n",
"8 True\n",
"9 True\n",
"10 True\n",
"11 False\n",
"12 False\n",
"13 True\n",
"14 False\n",
"15 False\n",
"16 False\n",
"17 False\n",
"18 False\n",
"19 False\n",
"20 True\n",
"21 False\n",
"22 True\n",
"Name: malfunction_binary, dtype: bool"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['malfunction_binary'] = data['Malfunction'] > 0\n",
"data['malfunction_binary']"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f55904844e0>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"%matplotlib inline\n",
"import seaborn as sns\n",
"sns.regplot(x='Temperature', y='malfunction_binary', data=data, logistic=True)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f55903c5198>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"sns.regplot(x='Pressure', y='malfunction_binary', data=data, logistic=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# /OWN ANALYSIS"
]
},
{
{
"cell_type": "markdown",
"cell_type": "markdown",
"metadata": {},
"metadata": {},
...
@@ -465,7 +589,6 @@
...
@@ -465,7 +589,6 @@
}
}
],
],
"source": [
"source": [
"%matplotlib inline\n",
"pd.set_option('mode.chained_assignment',None) # this removes a useless warning from pandas\n",
"pd.set_option('mode.chained_assignment',None) # this removes a useless warning from pandas\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
...
@@ -705,7 +828,7 @@
...
@@ -705,7 +828,7 @@
"name": "python",
"name": "python",
"nbconvert_exporter": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"pygments_lexer": "ipython3",
"version": "3.
7.3
"
"version": "3.
6.4
"
}
}
},
},
"nbformat": 4,
"nbformat": 4,
...
...
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