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SinrezSinrez
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Diff for: ‎pulsar.ipynb

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{
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"nbformat": 4,
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"metadata": {
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"colab": {
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"provenance": [],
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"authorship_tag": "ABX9TyMCs8ZurbYjKgvOK2E6DVNY",
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"include_colab_link": true
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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"name": "python"
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}
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/Sinrez/PythonProjects/blob/main/pulsar.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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{
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"cell_type": "code",
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"metadata": {
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},
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"id": "SWq3BNV2eRWO",
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"outputId": "4ddf0662-3d33-4149-a4f0-c1571322b6b0"
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},
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 17898 entries, 0 to 17897\n",
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"Data columns (total 9 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 MIP 17898 non-null float64\n",
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" 1 STDIP 17898 non-null float64\n",
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" 2 EKIP 17898 non-null float64\n",
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" 3 SIP 17898 non-null float64\n",
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" 7 SC 17898 non-null float64\n",
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" 8 TG 17898 non-null int64 \n",
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"dtypes: float64(8), int64(1)\n",
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"memory usage: 1.2 MB\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"data = pd.read_csv('/content/asset-v1_ITMOUniversity+DATSC+summer_2022_1+type@asset+block@pulsar_stars_new.csv')\n",
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"data.info()"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"data.head()"
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],
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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" MIP STDIP EKIP SIP MC STDC EKC \\\n",
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"0 140.562500 55.683782 -0.234571 -0.699648 3.199833 19.110426 7.975532 \n",
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"1 102.507812 58.882430 0.465318 -0.515088 1.677258 14.860146 10.576487 \n",
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"\n",
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" SC TG \n",
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"0 74.242225 0 \n",
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"1 127.393580 0 \n",
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" <th></th>\n",
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" <th>MIP</th>\n",
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" <th>STDIP</th>\n",
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" const element = document.querySelector('#df-f2850226-f07b-42b7-9513-775eb70439ca');\n",
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" const dataTable =\n",
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" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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" [key], {});\n",
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" if (!dataTable) return;\n",
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"\n",
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" const docLinkHtml = 'Like what you see? Visit the ' +\n",
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" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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" + ' to learn more about interactive tables.';\n",
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" element.innerHTML = '';\n",
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" dataTable['output_type'] = 'display_data';\n",
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" await google.colab.output.renderOutput(dataTable, element);\n",
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]
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"metadata": {},
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"execution_count": 5
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"sample_data = data[((data.TG == 0) & (data.MIP >= 73.4765625) & (data.MIP <= 77.6015625)) | ((data.TG == 1) & (data.MIP >= 25.890625) & (data.MIP <= 31.5078125))]\n",
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"sample_data.info()"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "RyO1fWR8iB04",
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"outputId": "912b06a5-7c9f-44fb-9c7a-f2efd5432582"
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},
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"<class 'pandas.core.frame.DataFrame'>\n",
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"Int64Index: 203 entries, 61 to 17711\n",
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"Data columns (total 9 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 MIP 203 non-null float64\n",
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" 1 STDIP 203 non-null float64\n",
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" 2 EKIP 203 non-null float64\n",
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" 3 SIP 203 non-null float64\n",
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" 4 MC 203 non-null float64\n",
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" 5 STDC 203 non-null float64\n",
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" 6 EKC 203 non-null float64\n",
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" 7 SC 203 non-null float64\n",
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" 8 TG 203 non-null int64 \n",
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"dtypes: float64(8), int64(1)\n",
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"memory usage: 15.9 KB\n"
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]
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}
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]
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},
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{
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"cell_type": "code",
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"source": [
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"mean_mip_rounded = round(sample_data['MIP'].mean(), 3)\n",
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"mean_mip_rounded"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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"id": "8goniWKdjnSx",
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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},
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"metadata": {},
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"execution_count": 12
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}
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]
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]
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}

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