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Dodanie notebook_intelligence itp

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sasza 2025-04-10 12:04:48 +02:00
parent 061cdb6aed
commit 92a2de0135
3 changed files with 151 additions and 195 deletions
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51
PythonAI/JupyterLab/Cwiczenie1.ipynb
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@ -18,7 +18,7 @@
},
{
"cell_type": "code",
"execution_count": 46,
"execution_count": 1,
"id": "178ae645-cad9-491c-9a26-c173eda34a00",
"metadata": {},
"outputs": [],
@ -29,7 +29,7 @@
},
{
"cell_type": "code",
"execution_count": 47,
"execution_count": 2,
"id": "540f9f69-d932-4b5a-8d70-e18a08bdcf46",
"metadata": {},
"outputs": [
@ -245,7 +245,7 @@
"[481 rows x 10 columns]"
]
},
"execution_count": 47,
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
@ -268,7 +268,7 @@
},
{
"cell_type": "code",
"execution_count": 48,
"execution_count": 3,
"id": "62e61e9e-f06e-4124-9a89-1979dc071c47",
"metadata": {},
"outputs": [
@ -496,7 +496,7 @@
"[481 rows x 11 columns]"
]
},
"execution_count": 48,
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
@ -521,7 +521,7 @@
},
{
"cell_type": "code",
"execution_count": 49,
"execution_count": 4,
"id": "e06e7a55-1837-456b-97ef-4c58276bd7b6",
"metadata": {},
"outputs": [
@ -749,7 +749,7 @@
"[481 rows x 11 columns]"
]
},
"execution_count": 49,
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@ -773,7 +773,7 @@
},
{
"cell_type": "code",
"execution_count": 50,
"execution_count": 5,
"id": "389f0c22-b210-45f3-9627-02c06d255a69",
"metadata": {},
"outputs": [],
@ -793,7 +793,7 @@
},
{
"cell_type": "code",
"execution_count": 51,
"execution_count": 6,
"id": "60362a3d-91ab-4666-bf26-afdeb3cd5c9c",
"metadata": {},
"outputs": [],
@ -805,7 +805,7 @@
},
{
"cell_type": "code",
"execution_count": 52,
"execution_count": 7,
"id": "a68dd0eb-6085-483b-99f3-a540a35515c2",
"metadata": {},
"outputs": [],
@ -821,7 +821,7 @@
},
{
"cell_type": "code",
"execution_count": 53,
"execution_count": 8,
"id": "bb4ecdab-2136-4970-a38d-6eaec8ec92ae",
"metadata": {},
"outputs": [
@ -1049,7 +1049,7 @@
"[481 rows x 11 columns]"
]
},
"execution_count": 53,
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
@ -1060,7 +1060,7 @@
},
{
"cell_type": "code",
"execution_count": 54,
"execution_count": 9,
"id": "7c8199c4-9d1b-405a-9cda-b2aef1616999",
"metadata": {},
"outputs": [
@ -1201,7 +1201,7 @@
"std 0.159067 0.0 0.159067 "
]
},
"execution_count": 54,
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@ -1212,7 +1212,7 @@
},
{
"cell_type": "code",
"execution_count": 55,
"execution_count": 10,
"id": "7468e63c-c72b-4150-bd40-6f6e61f17af8",
"metadata": {},
"outputs": [
@ -1381,7 +1381,7 @@
"std 0.176697 "
]
},
"execution_count": 55,
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@ -1400,7 +1400,7 @@
},
{
"cell_type": "code",
"execution_count": 45,
"execution_count": 12,
"id": "efa6a165-9988-420f-a2f0-10cbbcd5dc39",
"metadata": {},
"outputs": [
@ -1442,7 +1442,7 @@
" <td>249.000000</td>\n",
" <td>249.000000</td>\n",
" <td>249.000000</td>\n",
" <td>157.000000</td>\n",
" <td>249.000000</td>\n",
" <td>249.000000</td>\n",
" <td>249.000000</td>\n",
" <td>249.000000</td>\n",
@ -1454,7 +1454,7 @@
" <td>16.004819</td>\n",
" <td>6.371486</td>\n",
" <td>8.712048</td>\n",
" <td>0.364968</td>\n",
" <td>0.230120</td>\n",
" <td>4.080321</td>\n",
" <td>8.360241</td>\n",
" <td>0.048193</td>\n",
@ -1466,7 +1466,7 @@
" <td>11.000615</td>\n",
" <td>10.157809</td>\n",
" <td>9.936468</td>\n",
" <td>2.679477</td>\n",
" <td>2.132453</td>\n",
" <td>26.325536</td>\n",
" <td>15.718945</td>\n",
" <td>0.249368</td>\n",
@ -1539,9 +1539,9 @@
],
"text/plain": [
" TMAX TMIN TOBS WESF SNOW PRCP \\\n",
"count 249.000000 249.000000 249.000000 157.000000 249.000000 249.000000 \n",
"mean 16.004819 6.371486 8.712048 0.364968 4.080321 8.360241 \n",
"std 11.000615 10.157809 9.936468 2.679477 26.325536 15.718945 \n",
"count 249.000000 249.000000 249.000000 249.000000 249.000000 249.000000 \n",
"mean 16.004819 6.371486 8.712048 0.230120 4.080321 8.360241 \n",
"std 11.000615 10.157809 9.936468 2.132453 26.325536 15.718945 \n",
"min -11.700000 -17.200000 -16.100000 0.000000 0.000000 0.000000 \n",
"25% 6.700000 -1.700000 0.000000 0.000000 0.000000 0.000000 \n",
"50% 14.400000 5.600000 8.300000 0.000000 0.000000 0.300000 \n",
@ -1559,7 +1559,7 @@
"max 2.000000 1.000000 2.000000 "
]
},
"execution_count": 45,
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@ -1571,6 +1571,7 @@
"df_merged[\"incl_weather_true\"] = df_merged.incl_weather_true_temp.fillna(0) + df_merged.incl_weather_true_snow.fillna(0)\n",
"df_merged[\"incl_weather_false\"] = df_merged.incl_weather_false_temp.fillna(0) + df_merged.incl_weather_false_snow.fillna(0)\n",
"df_merged[\"snow\"] = df_merged.snow_temp.fillna(0) + df_merged.snow_snow.fillna(0)\n",
"df_merged['WESF'] = df_merged['WESF'].fillna(0)\n",
"\n",
"del df_merged[\"PRCP_temp\"]\n",
"del df_merged[\"PRCP_snow\"]\n",
@ -1612,7 +1613,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.2"
"version": "3.13.2"
}
},
"nbformat": 4,

2
PythonAI/JupyterLab/Pipfile
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@ -10,6 +10,8 @@ numpy = "*"
scikit-learn = "*"
sklearn-pandas = "*"
matplotlib = "*"
notebook-intelligence = "*"
jupyterlab-vim = "*"
[dev-packages]

293
PythonAI/JupyterLab/PythonAI_4.ipynb
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