{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "#@title IMPORT\n", "import io,os,re,sys,math,time,uuid,ctypes,pickle,psutil,random,shutil,string,urllib,decimal,datetime,itertools,traceback,collections,platform\n", "import matplotlib.pyplot as plt, seaborn as sns, plotly.express as px\n", "import numpy as np, pandas as pd\n", "\n", "write = print" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "<>:1: SyntaxWarning: invalid escape sequence '\\M'\n", "<>:1: SyntaxWarning: invalid escape sequence '\\M'\n", "C:\\Users\\f64\\AppData\\Local\\Temp\\ipykernel_28436\\1255343956.py:1: SyntaxWarning: invalid escape sequence '\\M'\n", " path1 = \"N:\\Makarov\\Development\\Python\\Jupiter Notebooks\\Gaziev CSV\\TestData_1504_AB_soloV_gaziev.zip\"\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " X Y Z A B V Vis Vfloat Vsign Vposneg\n", "0 222 473 0 -12 73 NaN 0 0.0 0 o\n", "1 212 425 202 24 15 NaN 0 0.0 0 o\n", "2 220 433 391 22 -22 NaN 0 0.0 0 o\n", "3 -212 475 229 65 -45 NaN 0 0.0 0 o\n", "4 202 513 111 16 28 NaN 0 0.0 0 o\n", "... ... ... ... .. .. ... ... ... ... ...\n", "12005 202 460 -37 20 -3 NaN 0 0.0 0 o\n", "12006 -211 543 19 23 14 NaN 0 0.0 0 o\n", "12007 202 609 208 -10 21 NaN 0 0.0 0 o\n", "12008 422 633 581 23 39 NaN 0 0.0 0 o\n", "12009 -232 601 732 54 52 NaN 0 0.0 0 o\n", "\n", "[12010 rows x 10 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path1 = \"N:\\Makarov\\Development\\Python\\Jupiter Notebooks\\Gaziev CSV\\TestData_1504_AB_soloV_gaziev.zip\"\n", "df = None\n", "if(os.path.exists(path1)):\n", " df = pd.read_csv(path1, sep=';',compression=\"zip\")\n", "if not df is None:\n", " df0 = df.copy()\n", " df0[\"Vis\"] = df0.V.map(lambda v: 0 if str(v)==\"nan\" else 1).astype(int)\n", " df0[\"Vfloat\"] = df0.V.map(lambda v: 0 if str(v)==\"nan\" else str(v).replace(',', '.')).astype(float)\n", " df0[\"Vsign\"] = df0.Vfloat.map(lambda v: -1 if v<0 else 1 if v>0 else 0).astype(int)\n", " df0[\"Vposneg\"] = df0.Vfloat.map(lambda v: \"n\" if v<0 else \"p\" if v>0 else \"o\").astype(str)\n", "df0" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "XYZABV_12010_264.CSV\n" ] } ], "source": [ "colnames = \"\".join(df.columns)\n", "if colnames.lower().startswith(\"xyz\"):\n", " colcounts = \"_\".join(map(str,sorted(set(df.notna().sum()), reverse=True)))\n", " fileXYZ = f\"{colnames}_{colcounts}.CSV\"\n", " write(fileXYZ)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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IDXYZ
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14AAA011112-320452-505
15AAA011112202486-547
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18AAA01111321392-205
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" ], "text/plain": [ " ID X Y Z\n", "1 AAA011111 111 702 536\n", "2 AAA011111 200 711 556\n", "3 AAA011111 -221 703 505\n", "4 AAA011111 -202 660 382\n", "5 AAA011111 -22 714 277\n", "6 AAA011111 211 746 312\n", "7 AAA011111 -200 732 257\n", "9 AAA011112 201 584 -36\n", "10 AAA011112 200 572 50\n", "11 AAA011112 -2 557 58\n", "12 AAA011112 -102 616 22\n", "13 AAA011112 -222 525 -178\n", "14 AAA011112 -320 452 -505\n", "15 AAA011112 202 486 -547\n", "17 AAA011113 -222 412 -343\n", "18 AAA011113 21 392 -205" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "path2 = r\"J:\\tmp\\Makarov\\Pack_01.csv\"\n", "path2 = r\"J:\\tmp\\Makarov\\Pack_02.csv\"\n", "xyz = [\"X\",\"Y\",\"Z\"]\n", "df2 = None\n", "if(os.path.exists(path2)):\n", " df2 = pd.read_csv(path2, sep=';', header=None)\n", " df2.columns = [\"ID\"] + xyz\n", " df2 = df2.query(\"ID!='ID'\")\n", "df2.head(16)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "set(dgID.apply(len))={7}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\f64\\AppData\\Local\\Temp\\ipykernel_28436\\1846308829.py:2: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " print(f\"{set(dgID.apply(len))=}\")\n", "C:\\Users\\f64\\AppData\\Local\\Temp\\ipykernel_28436\\1846308829.py:4: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " dgID.apply(len).reset_index()\n" ] }, { "data": { "text/html": [ "
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ID0
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" ], "text/plain": [ " ID 0\n", "0 AAA011111 7\n", "1 AAA011112 7\n", "2 AAA011113 7\n", "3 AAA011114 7\n", "4 AAA011115 7\n", "5 AAA011116 7\n", "6 AAA011117 7\n", "7 AAA011118 7\n", "8 BBB011111 7\n", "9 BBB011112 7\n", "10 BBB011113 7\n", "11 BBB011114 7\n", "12 BBB011115 7\n", "13 BBB011116 7\n", "14 BBB011117 7\n", "15 CCC011111 7\n", "16 CCC011112 7\n", "17 DDD011111 7\n", "18 DDD011112 7\n", "19 DDD011113 7" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dgID = df2.groupby(\"ID\") # , include_groups=False\n", "print(f\"{set(dgID.apply(len))=}\")\n", "dictGroupID = dict(list(dgID))\n", "dgID.apply(len).reset_index()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[['-300' '554' '-130' '222' '598' '-85' '221' '581' '-249' '113' '567'\n", " '-242' '-220' '561' '-13' '-102' '601' '258' '-221' '575' '438']]\n" ] }, { "data": { "text/html": [ "
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IDXYZ
113BBB011117-221575438
114BBB011117-102601258
115BBB011117-220561-13
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117BBB011117221581-249
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" ], "text/plain": [ " ID X Y Z\n", "113 BBB011117 -221 575 438\n", "114 BBB011117 -102 601 258\n", "115 BBB011117 -220 561 -13\n", "116 BBB011117 113 567 -242\n", "117 BBB011117 221 581 -249\n", "118 BBB011117 222 598 -85\n", "119 BBB011117 -300 554 -130" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#dgID.apply(lambda df: df.tail(1))\n", "#dgID.apply(lambda df: type(df))\n", "#dgID.apply(lambda df: list(df.columns))\n", "dgID.get_group(\"BBB011117\")\n", "#print(dictGroupID[\"BBB011117\"][xyz].values.reshape(1,-1))\n", "print(dictGroupID[\"BBB011117\"][xyz].values[::-1].reshape(1,-1))\n", "\n", "dictGroupID[\"BBB011117\"] # dgID.get_group(\"BBB011117\")\n", "#dgID.indices\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([['438', '575', '-221'],\n", " ['258', '601', '-102'],\n", " ['-13', '561', '-220'],\n", " ['-242', '567', '113'],\n", " ['-249', '581', '221'],\n", " ['-85', '598', '222'],\n", " ['-130', '554', '-300']], dtype=object)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dictGroupID[\"BBB011117\"][xyz].values[:,::-1]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\f64\\AppData\\Local\\Temp\\ipykernel_28436\\1487317556.py:1: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", " df_packs_reshaped = dgID.apply(lambda df: pd.Series(df[xyz].values[::-1].reshape(1,-1)[0])).reset_index() # правильный порядок\n" ] }, { "data": { "text/html": [ "
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