{ "cells": [ { "cell_type": "markdown", "id": "34b79531-3498-4b34-a147-ec21449f8816", "metadata": {}, "source": [ "# Dzień 3" ] }, { "cell_type": "markdown", "id": "24bfa403-53ee-4b10-ab9e-a90dd87ac184", "metadata": {}, "source": [ "## Data wrangling / prezentacje danych" ] }, { "cell_type": "code", "execution_count": null, "id": "0f8b7ac6-af7c-4f88-b878-4752c198f2c4", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 4, "id": "cba0359b-1e1d-4c97-ad8d-a52867071de0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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datedatatypestationattributesvalue
02018-01-01T00:00:00PRCPGHCND:US1CTFR0039,,N,0.0
12018-01-01T00:00:00PRCPGHCND:US1NJBG0015,,N,0.0
22018-01-01T00:00:00SNOWGHCND:US1NJBG0015,,N,0.0
32018-01-01T00:00:00PRCPGHCND:US1NJBG0017,,N,0.0
42018-01-01T00:00:00SNOWGHCND:US1NJBG0017,,N,0.0
..................
787752018-12-31T00:00:00WDF5GHCND:USW00094789,,W,130.0
787762018-12-31T00:00:00WSF2GHCND:USW00094789,,W,9.8
787772018-12-31T00:00:00WSF5GHCND:USW00094789,,W,12.5
787782018-12-31T00:00:00WT01GHCND:USW00094789,,W,1.0
787792018-12-31T00:00:00WT02GHCND:USW00094789,,W,1.0
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" ], "text/plain": [ " date datatype station attributes value\n", "0 2018-01-01T00:00:00 PRCP GHCND:US1CTFR0039 ,,N, 0.0\n", "1 2018-01-01T00:00:00 PRCP GHCND:US1NJBG0015 ,,N, 0.0\n", "2 2018-01-01T00:00:00 SNOW GHCND:US1NJBG0015 ,,N, 0.0\n", "3 2018-01-01T00:00:00 PRCP GHCND:US1NJBG0017 ,,N, 0.0\n", "4 2018-01-01T00:00:00 SNOW GHCND:US1NJBG0017 ,,N, 0.0\n", "... ... ... ... ... ...\n", "78775 2018-12-31T00:00:00 WDF5 GHCND:USW00094789 ,,W, 130.0\n", "78776 2018-12-31T00:00:00 WSF2 GHCND:USW00094789 ,,W, 9.8\n", "78777 2018-12-31T00:00:00 WSF5 GHCND:USW00094789 ,,W, 12.5\n", "78778 2018-12-31T00:00:00 WT01 GHCND:USW00094789 ,,W, 1.0\n", "78779 2018-12-31T00:00:00 WT02 GHCND:USW00094789 ,,W, 1.0\n", "\n", "[78780 rows x 5 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"data/nyc_weather_2018.csv\")\n", "df" ] }, { "cell_type": "code", "execution_count": null, "id": "89adf686-81fe-4aa8-8935-d3b91216d536", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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datedatatypestationattributesvalue
1142018-01-01T00:00:00SNOWGHCND:US1NYWC0019,,N,25.0
7892018-01-04T00:00:00SNOWGHCND:US1NYNS0007,,N,41.0
7942018-01-04T00:00:00SNOWGHCND:US1NYNS0018,,N,10.0
7982018-01-04T00:00:00SNOWGHCND:US1NYNS0024,,N,89.0
8002018-01-04T00:00:00SNOWGHCND:US1NYNS0030,,N,102.0
..................
686312018-11-16T00:00:00SNOWGHCND:US1NYRL0005,,N,170.0
686372018-11-16T00:00:00SNOWGHCND:US1NYWC0018,,N,191.0
748792018-12-14T00:00:00SNOWGHCND:US1NYWC0018,,N,3.0
771202018-12-24T00:00:00SNOWGHCND:US1NYWC0018,,N,18.0
784322018-12-30T00:00:00SNOWGHCND:US1NYWC0018,,N,5.0
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" ], "text/plain": [ " date datatype station attributes value\n", "114 2018-01-01T00:00:00 SNOW GHCND:US1NYWC0019 ,,N, 25.0\n", "789 2018-01-04T00:00:00 SNOW GHCND:US1NYNS0007 ,,N, 41.0\n", "794 2018-01-04T00:00:00 SNOW GHCND:US1NYNS0018 ,,N, 10.0\n", "798 2018-01-04T00:00:00 SNOW GHCND:US1NYNS0024 ,,N, 89.0\n", "800 2018-01-04T00:00:00 SNOW GHCND:US1NYNS0030 ,,N, 102.0\n", "... ... ... ... ... ...\n", "68631 2018-11-16T00:00:00 SNOW GHCND:US1NYRL0005 ,,N, 170.0\n", "68637 2018-11-16T00:00:00 SNOW GHCND:US1NYWC0018 ,,N, 191.0\n", "74879 2018-12-14T00:00:00 SNOW GHCND:US1NYWC0018 ,,N, 3.0\n", "77120 2018-12-24T00:00:00 SNOW GHCND:US1NYWC0018 ,,N, 18.0\n", "78432 2018-12-30T00:00:00 SNOW GHCND:US1NYWC0018 ,,N, 5.0\n", "\n", "[173 rows x 5 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.query(\n", " 'datatype == \"SNOW\" and value > 0'\n", " 'and station.str.contains(\"US1NY\")'\n", ")" ] }, { "cell_type": "markdown", "id": "d903b952-d663-4981-b68b-bf28cdd66d65", "metadata": {}, "source": [ "# Earthquakes" ] }, { "cell_type": "code", "execution_count": 4, "id": "194ee33c-e694-48ee-91a1-cc42b991c8ee", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 39, "id": "e0671fa8-749b-4f74-acc7-782c5dc86d60", "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv(\"data/earthquakes.csv\")" ] }, { "cell_type": "code", "execution_count": 14, "id": "8f569c5a-3cca-4b71-b1bb-3e53e62d8c39", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = df.mag\n", "fig,axes = plt.subplots(1,2,figsize=(12,4))\n", "for ax, bins in zip(axes, [7,35]):\n", " ax.hist(x,bins=bins)\n", " ax.set_title(f'{bins} bins')\n", "# plt.show()" ] }, { "cell_type": "code", "execution_count": 33, "id": "f343201e-180e-42a7-ae74-d6e4716c7197", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(3,3))\n", "outside = fig.add_axes([0,0,1,1])\n", "inside = fig.add_axes([0.2,0.6,0.2,0.2])" ] }, { "cell_type": "code", "execution_count": null, "id": "01a34069-53fa-470b-99a1-b2e1f50e5a12", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(8,8))\n", "gs = fig.add_gridspec(3,3)\n", "top_left = fig.add_subplot(gs[0,0])\n", "mid_left = fig.add_subplot(gs[1,0])\n", "top_right = fig.add_subplot(gs[:2,1:])\n", "bottom = fig.add_subplot(gs[2,:])" ] }, { "cell_type": "markdown", "id": "dae22787-4927-4491-bc31-2e5a0ff01542", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 44, "id": "177d2a31-a0d8-4d9b-ade3-2fdf5156baa9", "metadata": {}, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'ydata_profiling'", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[44]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[34;01mydata_profiling\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ProfileReport\n\u001b[32m 3\u001b[39m profile = ProfileReport(df)\n\u001b[32m 4\u001b[39m profile.to_file(\u001b[33m'\u001b[39m\u001b[33mreport.html\u001b[39m\u001b[33m'\u001b[39m)\n", "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'ydata_profiling'" ] } ], "source": [ "from ydata_profiling import ProfileReport\n", "\n", "profile = ProfileReport(df)\n", "profile.to_file('report.html')" ] }, { "cell_type": "markdown", "id": "25f38639-b9e3-4358-84b6-4068ca224aa4", "metadata": {}, "source": [ "# Polars" ] }, { "cell_type": "code", "execution_count": 2, "id": "d835bac6-aa5b-4f34-a7a4-7dec7aa1e146", "metadata": {}, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'polars'", "output_type": "error", "traceback": [ "\u001b[31m---------------------------------------------------------------------------\u001b[39m", "\u001b[31mModuleNotFoundError\u001b[39m Traceback (most recent call last)", "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[34;01mpolars\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[34;01mpl\u001b[39;00m\n\u001b[32m 3\u001b[39m \u001b[38;5;66;03m# create a DataFrame from a CSV file\u001b[39;00m\n\u001b[32m 4\u001b[39m df = pl.read_csv(\u001b[33m\"\u001b[39m\u001b[33mdata/earthquakes.csv\u001b[39m\u001b[33m\"\u001b[39m)\n", "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'polars'" ] } ], "source": [ "import polars as pl\n", "\n", "# create a DataFrame from a CSV file\n", "df = pl.read_csv(\"data/earthquakes.csv\")\n", "\n", "# filter the DataFrame to only include rows with magnitude greater than 7\n", "filtered_df = df[df[\"mag\"] > 7]\n", "\n", "# group the DataFrame by the \"location\" column and compute the sum of the \"mag\" column for each group\n", "grouped_df = filtered_df.groupby(\"location\").sum()\n", "\n", "# sort the grouped DataFrame by the sum of the \"mag\" column in descending order\n", "sorted_df = grouped_df.sort(by=\"mag\", ascending=False)\n", "\n", "# display the first 10 rows of the sorted DataFrame\n", "print(sorted_df[:10])\n" ] }, { "cell_type": "markdown", "id": "3861ff7e-23c0-4247-8c0c-17c1007d501a", "metadata": {}, "source": [ "## Praca domowa - plik taxi_trips (ma 80GB :D)" ] }, { "cell_type": "code", "execution_count": null, "id": "a0e609fb-2284-4b34-b2be-22f24474d515", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "07b61bfc-3319-4d37-b954-f5bec17c6d95", "metadata": {}, "source": [ "# Uczenie maszynowe" ] }, { "cell_type": "markdown", "id": "2a32ad88-3da9-41d5-b9b5-09583322113e", "metadata": {}, "source": [ "## Wprowadzenie\n", "### Definicja\n", "uczenie maszynowe to dziedzina sztucznej inteligencji - komputery uczą się na danych i potrafią podejmować decyzje bez programowania\n", "### Zastosowania\n", "filtr spamu, rozpoznawanie obrazów, rekomenracje produktów\n", "### Perceptron - najmniejsza jednostka obliczeniowa ludzkiego mózgu" ] }, { "cell_type": "markdown", "id": "ac24ed9e-629b-4146-9c65-a7f0ff47ab4c", "metadata": {}, "source": [ "## Biblioteki\n", "### TensorFlow\n", " - od Google\n", " - prostszy w obsłudze, łatwy do nauczenia się\n", "### PyTorch\n", " - od Facebooka\n", " - ma bardzo wysoki próg wejścia - wszystko robi się ręcznie, nie ma gotowców - trzeba umieć programować w Pythonie\n", "### scikit-learn\n", " - zbudowany na NumPy, SciPy i MatPlotLib" ] }, { "cell_type": "markdown", "id": "336d7f54-7373-435e-ba21-4cd5249909b7", "metadata": {}, "source": [ "## Uczenie nadzorowane\n", " - dane muszą być idealne" ] }, { "cell_type": "code", "execution_count": null, "id": "78f25a02-f822-477b-8f75-a8ec220a90ac", "metadata": {}, "outputs": [], "source": [ "import numpy as " ] }, { "cell_type": "code", "execution_count": null, "id": "f67a65d4-f8c6-4a83-92c8-efd0727e79c0", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.2" } }, "nbformat": 4, "nbformat_minor": 5 }