{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Работа с таблицами. Введение в библиотеку pandas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Опять импортируем библиотеку:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv(\"scores2.csv\", index_col=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Выбор столбцов и строк таблицы" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Выбор столбцов по названию**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Часто удобнее всего выбирать столбец по названию. Для этого достаточно указать название столбца в квадратных скобках (и обязательно в кавычках, так как название является строкой):" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id\n", "М141БПЛТЛ024 9\n", "М141БПЛТЛ031 10\n", "М141БПЛТЛ075 9\n", "М141БПЛТЛ017 9\n", "М141БПЛТЛ069 10\n", "М141БПЛТЛ072 9\n", "М141БПЛТЛ020 7\n", "М141БПЛТЛ026 10\n", "М141БПЛТЛ073 9\n", "М141БПЛТЛ078 6\n", "М141БПЛТЛ060 8\n", "М141БПЛТЛ040 9\n", "М141БПЛТЛ065 9\n", "М141БПЛТЛ053 7\n", "М141БПЛТЛ015 9\n", "М141БПЛТЛ021 9\n", "М141БПЛТЛ018 7\n", "М141БПЛТЛ039 8\n", "М141БПЛТЛ036 10\n", "М141БПЛТЛ049 7\n", "06114043 8\n", "М141БПЛТЛ048 6\n", "М141БПЛТЛ034 9\n", "М141БПЛТЛ045 8\n", "М141БПЛТЛ033 9\n", "М141БПЛТЛ083 5\n", "М141БПЛТЛ008 8\n", "М141БПЛТЛ001 7\n", "М141БПЛТЛ038 9\n", "М141БПЛТЛ052 7\n", "М141БПЛТЛ011 6\n", "М141БПЛТЛ004 7\n", "М141БПЛТЛ010 6\n", "М141БПЛТЛ071 9\n", "М141БПЛТЛ035 6\n", "М141БПЛТЛ030 6\n", "М141БПЛТЛ070 5\n", "М141БПЛТЛ051 9\n", "М141БПЛТЛ046 7\n", "М141БПЛТЛ047 8\n", "М141БПЛТЛ063 5\n", "М141БПЛТЛ029 8\n", "М141БПЛТЛ064 8\n", "М141БПЛТЛ076 7\n", "М141БПЛТЛ062 7\n", "М141БПЛТЛ074 6\n", "130232038 7\n", "М141БПЛТЛ023 9\n", "М141БПЛТЛ054 8\n", "М141БПЛТЛ012 6\n", "М141БПЛТЛ006 5\n", "М141БПЛТЛ055 5\n", "М141БПЛТЛ007 7\n", "М141БПЛТЛ050 6\n", "М141БПЛТЛ066 10\n", "М141БПЛТЛ043 5\n", "М141БПЛТЛ084 7\n", "М141БПЛТЛ005 7\n", "М141БПЛТЛ044 5\n", "13051038 4\n", "Name: mstat, dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['mstat']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Еще столбец можно выбрать, не используя квадратные скобки, а просто указав его название через точку: " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id\n", "М141БПЛТЛ024 9\n", "М141БПЛТЛ031 10\n", "М141БПЛТЛ075 9\n", "М141БПЛТЛ017 9\n", "М141БПЛТЛ069 10\n", "М141БПЛТЛ072 9\n", "М141БПЛТЛ020 7\n", "М141БПЛТЛ026 10\n", "М141БПЛТЛ073 9\n", "М141БПЛТЛ078 6\n", "М141БПЛТЛ060 8\n", "М141БПЛТЛ040 9\n", "М141БПЛТЛ065 9\n", "М141БПЛТЛ053 7\n", "М141БПЛТЛ015 9\n", "М141БПЛТЛ021 9\n", "М141БПЛТЛ018 7\n", "М141БПЛТЛ039 8\n", "М141БПЛТЛ036 10\n", "М141БПЛТЛ049 7\n", "06114043 8\n", "М141БПЛТЛ048 6\n", "М141БПЛТЛ034 9\n", "М141БПЛТЛ045 8\n", "М141БПЛТЛ033 9\n", "М141БПЛТЛ083 5\n", "М141БПЛТЛ008 8\n", "М141БПЛТЛ001 7\n", "М141БПЛТЛ038 9\n", "М141БПЛТЛ052 7\n", "М141БПЛТЛ011 6\n", "М141БПЛТЛ004 7\n", "М141БПЛТЛ010 6\n", "М141БПЛТЛ071 9\n", "М141БПЛТЛ035 6\n", "М141БПЛТЛ030 6\n", "М141БПЛТЛ070 5\n", "М141БПЛТЛ051 9\n", "М141БПЛТЛ046 7\n", "М141БПЛТЛ047 8\n", "М141БПЛТЛ063 5\n", "М141БПЛТЛ029 8\n", "М141БПЛТЛ064 8\n", "М141БПЛТЛ076 7\n", "М141БПЛТЛ062 7\n", "М141БПЛТЛ074 6\n", "130232038 7\n", "М141БПЛТЛ023 9\n", "М141БПЛТЛ054 8\n", "М141БПЛТЛ012 6\n", "М141БПЛТЛ006 5\n", "М141БПЛТЛ055 5\n", "М141БПЛТЛ007 7\n", "М141БПЛТЛ050 6\n", "М141БПЛТЛ066 10\n", "М141БПЛТЛ043 5\n", "М141БПЛТЛ084 7\n", "М141БПЛТЛ005 7\n", "М141БПЛТЛ044 5\n", "13051038 4\n", "Name: mstat, dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.mstat" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Однако такой способ не универсален. В случае, если в названии столбца используются недопустимые для переменных символы (пробелы, тире, кириллические буквы), этот метод не подойдет. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Если нам нужно выбрать более одного столбца, то названия столбцов указываются внутри списка ‒ появляются двойные квадратные скобки:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'df2' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"soc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"polsoc\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'df2' is not defined" ] } ], "source": [ "df2[[\"soc\", \"polsoc\"]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Если нам нужно несколько столбцов подряд, начиная с одного названия и заканчивая другим, можно воспользоваться методом `.loc`: " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n", "
" ], "text/plain": [ " econ eng polth mstat2 phist law\n", "id \n", "М141БПЛТЛ024 8 9 8 10 8.0 7\n", "М141БПЛТЛ031 10 10 10 10 9.0 9\n", "М141БПЛТЛ075 10 9 10 9 8.0 9\n", "М141БПЛТЛ017 8 9 9 10 6.0 9\n", "М141БПЛТЛ069 10 10 10 9 8.0 8\n", "М141БПЛТЛ072 10 9 8 9 8.0 8\n", "М141БПЛТЛ020 6 9 10 8 8.0 7\n", "М141БПЛТЛ026 7 10 7 9 8.0 8\n", "М141БПЛТЛ073 8 9 8 9 8.0 8\n", "М141БПЛТЛ078 5 6 10 7 6.0 8\n", "М141БПЛТЛ060 7 9 8 8 5.0 7\n", "М141БПЛТЛ040 6 9 7 8 6.0 9\n", "М141БПЛТЛ065 4 8 8 7 9.0 8\n", "М141БПЛТЛ053 5 9 8 7 8.0 8\n", "М141БПЛТЛ015 6 9 7 9 4.0 7\n", "М141БПЛТЛ021 8 9 8 8 7.0 7\n", "М141БПЛТЛ018 7 9 7 8 6.0 6\n", "М141БПЛТЛ039 8 8 8 6 8.0 7\n", "М141БПЛТЛ036 8 8 6 9 4.0 8\n", "М141БПЛТЛ049 6 8 6 8 4.0 8\n", "06114043 5 8 8 8 10.0 7\n", "М141БПЛТЛ048 6 9 6 4 4.0 6\n", "М141БПЛТЛ034 6 9 6 8 6.0 7\n", "М141БПЛТЛ045 7 8 6 7 6.0 7\n", "М141БПЛТЛ033 7 9 7 9 7.0 7\n", "М141БПЛТЛ083 5 8 7 6 5.0 7\n", "М141БПЛТЛ008 9 8 10 9 8.0 9\n", "М141БПЛТЛ001 4 10 7 7 6.0 8\n", "М141БПЛТЛ038 4 9 6 7 6.0 7\n", "М141БПЛТЛ052 7 8 6 6 6.0 8\n", "М141БПЛТЛ011 6 9 6 6 5.0 6\n", "М141БПЛТЛ004 6 8 6 6 5.0 5\n", "М141БПЛТЛ010 6 9 7 7 6.0 7\n", "М141БПЛТЛ071 7 9 6 8 4.0 6\n", "М141БПЛТЛ035 6 8 5 5 4.0 6\n", "М141БПЛТЛ030 6 7 6 6 4.0 8\n", "М141БПЛТЛ070 4 8 6 5 5.0 6\n", "М141БПЛТЛ051 6 8 7 6 7.0 6\n", "М141БПЛТЛ046 4 7 5 8 5.0 7\n", "М141БПЛТЛ047 4 7 5 9 5.0 6\n", "М141БПЛТЛ063 4 8 4 4 4.0 5\n", "М141БПЛТЛ029 7 9 5 6 7.0 6\n", "М141БПЛТЛ064 7 6 6 8 4.0 6\n", "М141БПЛТЛ076 6 8 6 6 6.0 8\n", "М141БПЛТЛ062 6 9 6 6 5.0 6\n", "М141БПЛТЛ074 4 7 6 5 6.0 6\n", "130232038 5 8 4 8 4.0 8\n", "М141БПЛТЛ023 8 9 6 9 4.0 7\n", "М141БПЛТЛ054 4 8 6 4 4.0 6\n", "М141БПЛТЛ012 4 10 6 5 4.0 7\n", "М141БПЛТЛ006 5 8 5 5 5.0 6\n", "М141БПЛТЛ055 4 7 7 4 8.0 5\n", "М141БПЛТЛ007 6 7 6 7 4.0 5\n", "М141БПЛТЛ050 6 8 4 5 4.0 5\n", "М141БПЛТЛ066 7 9 5 8 4.0 6\n", "М141БПЛТЛ043 5 8 5 6 5.0 6\n", "М141БПЛТЛ084 4 8 5 5 NaN 8\n", "М141БПЛТЛ005 5 7 4 7 4.0 5\n", "М141БПЛТЛ044 4 6 4 4 5.0 4\n", "13051038 4 9 5 5 5.0 5" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[:, 'econ' : 'law']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Откуда в квадратных скобках взялось двоеточие? Дело в том, что метод `.loc` ‒ более универсальный, и позволяет выбирать не только столбцы, но и строки. При этом нужные строки указываются на первом месте, а столбцы ‒ на втором. Когда мы пишем `.loc[:, 1]`, мы сообщаем Python, что нам нужны все строки (`:`) и столбцы, начиная с `Econ` и до `Law` включительно.\n", "\n", "**Внимание:** выбор столбцов по названиям через двоеточие очень напоминает срезы (*slices*) в списках. Но есть важное отличие. В случае текстовых названий, оба конца среза (левый и правый) включаются. Если бы срезы по названиям были бы устроены как срезы по числовым индексам, код выше выдавал бы столбцы с `Econ` и до `Phist`, не включая колонку `Law`, так как в обычных срезах правый конец исключается." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Выбор столбцов по номеру**\n", "\n", "Иногда может возникнуть необходимость выбрать столбец по его порядковому номеру. Например, когда названий столбцов нет как таковых или когда названия слишком длинные, а переименовывать их нежелательно. Сделать это можно с помощью метода `.iloc`:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id\n", "М141БПЛТЛ024 9\n", "М141БПЛТЛ031 10\n", "М141БПЛТЛ075 9\n", "М141БПЛТЛ017 9\n", "М141БПЛТЛ069 10\n", "М141БПЛТЛ072 9\n", "М141БПЛТЛ020 7\n", "М141БПЛТЛ026 10\n", "М141БПЛТЛ073 9\n", "М141БПЛТЛ078 6\n", "М141БПЛТЛ060 8\n", "М141БПЛТЛ040 9\n", "М141БПЛТЛ065 9\n", "М141БПЛТЛ053 7\n", "М141БПЛТЛ015 9\n", "М141БПЛТЛ021 9\n", "М141БПЛТЛ018 7\n", "М141БПЛТЛ039 8\n", "М141БПЛТЛ036 10\n", "М141БПЛТЛ049 7\n", "06114043 8\n", "М141БПЛТЛ048 6\n", "М141БПЛТЛ034 9\n", "М141БПЛТЛ045 8\n", "М141БПЛТЛ033 9\n", "М141БПЛТЛ083 5\n", "М141БПЛТЛ008 8\n", "М141БПЛТЛ001 7\n", "М141БПЛТЛ038 9\n", "М141БПЛТЛ052 7\n", "М141БПЛТЛ011 6\n", "М141БПЛТЛ004 7\n", "М141БПЛТЛ010 6\n", "М141БПЛТЛ071 9\n", "М141БПЛТЛ035 6\n", "М141БПЛТЛ030 6\n", "М141БПЛТЛ070 5\n", "М141БПЛТЛ051 9\n", "М141БПЛТЛ046 7\n", "М141БПЛТЛ047 8\n", "М141БПЛТЛ063 5\n", "М141БПЛТЛ029 8\n", "М141БПЛТЛ064 8\n", "М141БПЛТЛ076 7\n", "М141БПЛТЛ062 7\n", "М141БПЛТЛ074 6\n", "130232038 7\n", "М141БПЛТЛ023 9\n", "М141БПЛТЛ054 8\n", "М141БПЛТЛ012 6\n", "М141БПЛТЛ006 5\n", "М141БПЛТЛ055 5\n", "М141БПЛТЛ007 7\n", "М141БПЛТЛ050 6\n", "М141БПЛТЛ066 10\n", "М141БПЛТЛ043 5\n", "М141БПЛТЛ084 7\n", "М141БПЛТЛ005 7\n", "М141БПЛТЛ044 5\n", "13051038 4\n", "Name: mstat, dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[:, 1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Синтаксис кода с `.iloc` несильно отличается от синтаксиса `.loc`. В чем разница? Разница заключается в том, что метод `.loc` работает с текстовыми названиями, а метод `.iloc` ‒ с числовыми индексами. Отсюда и префикс `i` в названии (*i* ‒ индекс, *loc* ‒ location). Если мы попытаемся в `.iloc` указать названия столбцов, Python выдаст ошибку:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "cannot do slice indexing on with these indexers [mstat] of ", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'mstat'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'econ'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1470\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1471\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1472\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1473\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1474\u001b[0m \u001b[0;31m# we by definition only have the 0th axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m 2027\u001b[0m \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2028\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2029\u001b[0;31m \u001b[0mretval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mretval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2030\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2031\u001b[0m \u001b[0;31m# if the dim was reduced, then pass a lower-dim the next time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 2078\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2079\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2080\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_slice_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2081\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2082\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_slice_axis\u001b[0;34m(self, slice_obj, axis)\u001b[0m\n\u001b[1;32m 2046\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2047\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2048\u001b[0;31m \u001b[0mslice_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_slice_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mslice_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2049\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mslice_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2050\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mslice_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'iloc'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_convert_slice_indexer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 264\u001b[0m \u001b[0;31m# if we are accessing via lowered dim, use the last dim\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 265\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 266\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_slice_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 267\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 268\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_has_valid_setitem_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_convert_slice_indexer\u001b[0;34m(self, key, kind)\u001b[0m\n\u001b[1;32m 1688\u001b[0m \u001b[0;31m# validate iloc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1689\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'iloc'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1690\u001b[0;31m return slice(self._validate_indexer('slice', key.start, kind),\n\u001b[0m\u001b[1;32m 1691\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'slice'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1692\u001b[0m self._validate_indexer('slice', key.step, kind))\n", "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_validate_indexer\u001b[0;34m(self, form, key, kind)\u001b[0m\n\u001b[1;32m 4126\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4127\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mkind\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'iloc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'getitem'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4128\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalid_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mform\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4129\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4130\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_invalid_indexer\u001b[0;34m(self, form, key)\u001b[0m\n\u001b[1;32m 1846\u001b[0m \"indexers [{key}] of {kind}\".format(\n\u001b[1;32m 1847\u001b[0m \u001b[0mform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mform\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1848\u001b[0;31m kind=type(key)))\n\u001b[0m\u001b[1;32m 1849\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1850\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_duplicates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: cannot do slice indexing on with these indexers [mstat] of " ] } ], "source": [ "df.iloc[:, 'mstat': 'econ']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python пишет, что невозможно взять срез по индексам, которые имеют строковый тип (`class 'str'`), так как в квадратных скобках ожидаются числовые (целочисленные) индексы.\n", "\n", "Если нужно выбрать несколько столбцов подряд, можно воспользоваться срезами:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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mstatsoc
id
М141БПЛТЛ02498
М141БПЛТЛ0311010
М141БПЛТЛ07599
М141БПЛТЛ01798
М141БПЛТЛ0691010
М141БПЛТЛ07298
М141БПЛТЛ02077
М141БПЛТЛ026108
М141БПЛТЛ07398
М141БПЛТЛ07869
М141БПЛТЛ06087
М141БПЛТЛ04098
М141БПЛТЛ06598
М141БПЛТЛ05377
М141БПЛТЛ01597
М141БПЛТЛ02198
М141БПЛТЛ01879
М141БПЛТЛ03989
М141БПЛТЛ036107
М141БПЛТЛ04976
06114043810
М141БПЛТЛ04868
М141БПЛТЛ03497
М141БПЛТЛ04588
М141БПЛТЛ03398
М141БПЛТЛ08356
М141БПЛТЛ00888
М141БПЛТЛ00177
М141БПЛТЛ03896
М141БПЛТЛ05277
М141БПЛТЛ01168
М141БПЛТЛ00476
М141БПЛТЛ01067
М141БПЛТЛ07197
М141БПЛТЛ03567
М141БПЛТЛ03066
М141БПЛТЛ07056
М141БПЛТЛ05198
М141БПЛТЛ04677
М141БПЛТЛ04786
М141БПЛТЛ06356
М141БПЛТЛ02988
М141БПЛТЛ06486
М141БПЛТЛ07678
М141БПЛТЛ06277
М141БПЛТЛ07467
13023203876
М141БПЛТЛ02396
М141БПЛТЛ05486
М141БПЛТЛ01267
М141БПЛТЛ00656
М141БПЛТЛ05556
М141БПЛТЛ00777
М141БПЛТЛ05066
М141БПЛТЛ066107
М141БПЛТЛ04356
М141БПЛТЛ08478
М141БПЛТЛ00575
М141БПЛТЛ04457
1305103844
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" ], "text/plain": [ " mstat soc\n", "id \n", "М141БПЛТЛ024 9 8\n", "М141БПЛТЛ031 10 10\n", "М141БПЛТЛ075 9 9\n", "М141БПЛТЛ017 9 8\n", "М141БПЛТЛ069 10 10\n", "М141БПЛТЛ072 9 8\n", "М141БПЛТЛ020 7 7\n", "М141БПЛТЛ026 10 8\n", "М141БПЛТЛ073 9 8\n", "М141БПЛТЛ078 6 9\n", "М141БПЛТЛ060 8 7\n", "М141БПЛТЛ040 9 8\n", "М141БПЛТЛ065 9 8\n", "М141БПЛТЛ053 7 7\n", "М141БПЛТЛ015 9 7\n", "М141БПЛТЛ021 9 8\n", "М141БПЛТЛ018 7 9\n", "М141БПЛТЛ039 8 9\n", "М141БПЛТЛ036 10 7\n", "М141БПЛТЛ049 7 6\n", "06114043 8 10\n", "М141БПЛТЛ048 6 8\n", "М141БПЛТЛ034 9 7\n", "М141БПЛТЛ045 8 8\n", "М141БПЛТЛ033 9 8\n", "М141БПЛТЛ083 5 6\n", "М141БПЛТЛ008 8 8\n", "М141БПЛТЛ001 7 7\n", "М141БПЛТЛ038 9 6\n", "М141БПЛТЛ052 7 7\n", "М141БПЛТЛ011 6 8\n", "М141БПЛТЛ004 7 6\n", "М141БПЛТЛ010 6 7\n", "М141БПЛТЛ071 9 7\n", "М141БПЛТЛ035 6 7\n", "М141БПЛТЛ030 6 6\n", "М141БПЛТЛ070 5 6\n", "М141БПЛТЛ051 9 8\n", "М141БПЛТЛ046 7 7\n", "М141БПЛТЛ047 8 6\n", "М141БПЛТЛ063 5 6\n", "М141БПЛТЛ029 8 8\n", "М141БПЛТЛ064 8 6\n", "М141БПЛТЛ076 7 8\n", "М141БПЛТЛ062 7 7\n", "М141БПЛТЛ074 6 7\n", "130232038 7 6\n", "М141БПЛТЛ023 9 6\n", "М141БПЛТЛ054 8 6\n", "М141БПЛТЛ012 6 7\n", "М141БПЛТЛ006 5 6\n", "М141БПЛТЛ055 5 6\n", "М141БПЛТЛ007 7 7\n", "М141БПЛТЛ050 6 6\n", "М141БПЛТЛ066 10 7\n", "М141БПЛТЛ043 5 6\n", "М141БПЛТЛ084 7 8\n", "М141БПЛТЛ005 7 5\n", "М141БПЛТЛ044 5 7\n", "13051038 4 4" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[:, 1:3]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Числовые срезы в pandas уже ничем не отличаются от списковых срезов: правый конец среза не включается. В нашем случае мы выбрали только столбцы с индексами 1 и 2." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Выбор строк по названию**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Выбор строки по названию происходит аналогичным образом, только здесь метод `.loc` уже обязателен." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "catps 8.0\n", "mstat 10.0\n", "soc 10.0\n", "econ 10.0\n", "eng 10.0\n", "polth 10.0\n", "mstat2 10.0\n", "phist 9.0\n", "law 9.0\n", "phil 10.0\n", "polsoc 10.0\n", "ptheo 9.0\n", "preg 8.0\n", "compp 8.0\n", "game 9.0\n", "wpol 10.0\n", "male 1.0\n", "Name: М141БПЛТЛ031, dtype: float64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc['М141БПЛТЛ031'] # строка для студента с номером М141БПЛТЛ031" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "При этом ставить запятую и двоеточие, показывая, что нам нужна одна строка и все столбцы, уже не нужно. Если нам нужно выбрать несколько строк подряд, то `.loc` не нужен:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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catpsmstatsoceconengpolthmstat2phistlawphilpolsocptheopregcomppgamewpolmale
id
М141БПЛТЛ024798898108.07997.088.06101
М141БПЛТЛ03181010101010109.0910109.088.09101
М141БПЛТЛ0759991091098.091099.088.0791
М141БПЛТЛ017998899106.09998.088.0890
М141БПЛТЛ06910101010101098.081097.065.08101
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" ], "text/plain": [ " catps mstat soc econ eng polth mstat2 phist law phil \\\n", "id \n", "М141БПЛТЛ024 7 9 8 8 9 8 10 8.0 7 9 \n", "М141БПЛТЛ031 8 10 10 10 10 10 10 9.0 9 10 \n", "М141БПЛТЛ075 9 9 9 10 9 10 9 8.0 9 10 \n", "М141БПЛТЛ017 9 9 8 8 9 9 10 6.0 9 9 \n", "М141БПЛТЛ069 10 10 10 10 10 10 9 8.0 8 10 \n", "\n", " polsoc ptheo preg compp game wpol male \n", "id \n", "М141БПЛТЛ024 9 7.0 8 8.0 6 10 1 \n", "М141БПЛТЛ031 10 9.0 8 8.0 9 10 1 \n", "М141БПЛТЛ075 9 9.0 8 8.0 7 9 1 \n", "М141БПЛТЛ017 9 8.0 8 8.0 8 9 0 \n", "М141БПЛТЛ069 9 7.0 6 5.0 8 10 1 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"М141БПЛТЛ024\":'М141БПЛТЛ069']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Как Python понимает, что мы просим вывести именно строки с такими названиями, а не столбцы? Потому что у нас стоят одинарные квадратные скобки, а не двойные, как в случае со столбцами. (Да, в pandas много всяких тонкостей, но чтобы хорошо в них разбираться, нужно просто попрактиковаться и привыкнуть).\n", "\n", "Обратите внимание: разницы между двойными и одинарными кавычками нет, строки можно вводить в любых кавычках, как в примере выше." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Выбор строк по номеру**\n", "\n", "В этом случае достаточно указать номер в квадратных скобках в `.iloc`:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "catps 9.0\n", "mstat 9.0\n", "soc 9.0\n", "econ 10.0\n", "eng 9.0\n", "polth 10.0\n", "mstat2 9.0\n", "phist 8.0\n", "law 9.0\n", "phil 10.0\n", "polsoc 9.0\n", "ptheo 9.0\n", "preg 8.0\n", "compp 8.0\n", "game 7.0\n", "wpol 9.0\n", "male 1.0\n", "Name: М141БПЛТЛ075, dtype: float64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[2]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Если нужно несколько строк подряд, можно воспользоваться срезами:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
catpsmstatsoceconengpolthmstat2phistlawphilpolsocptheopregcomppgamewpolmale
id
М141БПЛТЛ03181010101010109.0910109.088.09101
М141БПЛТЛ0759991091098.091099.088.0791
\n", "
" ], "text/plain": [ " catps mstat soc econ eng polth mstat2 phist law phil \\\n", "id \n", "М141БПЛТЛ031 8 10 10 10 10 10 10 9.0 9 10 \n", "М141БПЛТЛ075 9 9 9 10 9 10 9 8.0 9 10 \n", "\n", " polsoc ptheo preg compp game wpol male \n", "id \n", "М141БПЛТЛ031 10 9.0 8 8.0 9 10 1 \n", "М141БПЛТЛ075 9 9.0 8 8.0 7 9 1 " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[1:3] # и без iloc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Если нужно несколько строк не подряд, можно просто перечислить внутри списка в `.iloc`:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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catpsmstatsoceconengpolthmstat2phistlawphilpolsocptheopregcomppgamewpolmale
id
М141БПЛТЛ03181010101010109.0910109.088.09101
М141БПЛТЛ0759991091098.091099.088.0791
М141БПЛТЛ0721098109898.081097.088.0990
М141БПЛТЛ06078779885.07585.078.0791
\n", "
" ], "text/plain": [ " catps mstat soc econ eng polth mstat2 phist law phil \\\n", "id \n", "М141БПЛТЛ031 8 10 10 10 10 10 10 9.0 9 10 \n", "М141БПЛТЛ075 9 9 9 10 9 10 9 8.0 9 10 \n", "М141БПЛТЛ072 10 9 8 10 9 8 9 8.0 8 10 \n", "М141БПЛТЛ060 7 8 7 7 9 8 8 5.0 7 5 \n", "\n", " polsoc ptheo preg compp game wpol male \n", "id \n", "М141БПЛТЛ031 10 9.0 8 8.0 9 10 1 \n", "М141БПЛТЛ075 9 9.0 8 8.0 7 9 1 \n", "М141БПЛТЛ072 9 7.0 8 8.0 9 9 0 \n", "М141БПЛТЛ060 8 5.0 7 8.0 7 9 1 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[[1,2,5,10]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Выбор наблюдений по названиям строк и столбцов**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Если нам нужно выбрать одно наблюдение на пересечении строки и столбца, можно воспользоваться методом `.at`: сначала указать название строки, потом ‒ столбца:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.at['М141БПЛТЛ078', 'game'] # оценка по теории игр у студента М141БПЛТЛ078" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Кроме того, можно применить метод `.loc`:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "9" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[\"М141БПЛТЛ075\", \"soc\"] # оценка по социологии у студента М141БПЛТЛ075" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "В чем разница между `.at` и `.loc`? Метод `.loc` более универсален. В то время как `.at` используется для нахождения *одного* наблюдения на пересечении строки и столбца, `.loc` позволяет выбрать несколько наблюдений (строк и столбцов) сразу. Например, так:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id\n", "М141БПЛТЛ024 9\n", "М141БПЛТЛ031 10\n", "М141БПЛТЛ075 9\n", "М141БПЛТЛ017 9\n", "М141БПЛТЛ069 10\n", "М141БПЛТЛ072 9\n", "М141БПЛТЛ020 7\n", "М141БПЛТЛ026 10\n", "М141БПЛТЛ073 9\n", "Name: mstat, dtype: int64" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[\"М141БПЛТЛ024\":\"М141БПЛТЛ073\", \"mstat\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Если нужно выбрать какое-то одно значение, метод `.at` будет работать более быстро, чем `.loc`. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Выбор наблюдения по номеру строки и столбца **" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Выбор наблюдения по номеру строки и столбца осуществляется аналогичным образом, только теперь мы используем методы с префиксом `i` для индексов: `.iat` и `.iloc`." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "9" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iat[4, 6] # оценка на пересечении строки 4 и столбца 6" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "9" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[8, 4] # оценка на пересечении строки 8 и столбца 4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Убедимся, что все верно:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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catpsmstatsoceconengpolthmstat2phistlawphilpolsocptheopregcomppgamewpolmale
id
М141БПЛТЛ024798898108.07997.088.06101
М141БПЛТЛ03181010101010109.0910109.088.09101
М141БПЛТЛ0759991091098.091099.088.0791
М141БПЛТЛ017998899106.09998.088.0890
М141БПЛТЛ06910101010101098.081097.065.08101
М141БПЛТЛ0721098109898.081097.088.0990
М141БПЛТЛ020877691088.07797.086.0891
М141БПЛТЛ0267108710798.08888.087.0780
\n", "
" ], "text/plain": [ " catps mstat soc econ eng polth mstat2 phist law phil \\\n", "id \n", "М141БПЛТЛ024 7 9 8 8 9 8 10 8.0 7 9 \n", "М141БПЛТЛ031 8 10 10 10 10 10 10 9.0 9 10 \n", "М141БПЛТЛ075 9 9 9 10 9 10 9 8.0 9 10 \n", "М141БПЛТЛ017 9 9 8 8 9 9 10 6.0 9 9 \n", "М141БПЛТЛ069 10 10 10 10 10 10 9 8.0 8 10 \n", "М141БПЛТЛ072 10 9 8 10 9 8 9 8.0 8 10 \n", "М141БПЛТЛ020 8 7 7 6 9 10 8 8.0 7 7 \n", "М141БПЛТЛ026 7 10 8 7 10 7 9 8.0 8 8 \n", "\n", " polsoc ptheo preg compp game wpol male \n", "id \n", "М141БПЛТЛ024 9 7.0 8 8.0 6 10 1 \n", "М141БПЛТЛ031 10 9.0 8 8.0 9 10 1 \n", "М141БПЛТЛ075 9 9.0 8 8.0 7 9 1 \n", "М141БПЛТЛ017 9 8.0 8 8.0 8 9 0 \n", "М141БПЛТЛ069 9 7.0 6 5.0 8 10 1 \n", "М141БПЛТЛ072 9 7.0 8 8.0 9 9 0 \n", "М141БПЛТЛ020 9 7.0 8 6.0 8 9 1 \n", "М141БПЛТЛ026 8 8.0 8 7.0 7 8 0 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Выбор строк по условию (фильтрация наблюдений)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Часто в исследованиях нас не интересует выбор отдельных строк по названию или номеру, мы хотим отбирать строки в таблице согласно некорому условию (условиям). Другими словами, проводить фильтрацию наблюдений. Для этого интересующее нас условие необходимо указать в квадратных скобках. Выберем из датафрейма `df`строки, которые соответствуют студентам с оценкой по экономике выше 6." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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catpsmstatsoceconengpolthmstat2phistlawphilpolsocptheopregcomppgamewpolmale
id
М141БПЛТЛ024798898108.07997.088.06101
М141БПЛТЛ03181010101010109.0910109.088.09101
М141БПЛТЛ0759991091098.091099.088.0791
М141БПЛТЛ017998899106.09998.088.0890
М141БПЛТЛ06910101010101098.081097.065.08101
М141БПЛТЛ0721098109898.081097.088.0990
М141БПЛТЛ0267108710798.08888.087.0780
М141БПЛТЛ07379889898.08997.076.01091
М141БПЛТЛ06078779885.07585.078.0791
М141БПЛТЛ02189889887.07766.086.0780
М141БПЛТЛ01877979786.06787.077.0780
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" ], "text/plain": [ " catps mstat soc econ eng polth mstat2 phist law phil \\\n", "id \n", "М141БПЛТЛ008 10 8 8 9 8 10 9 8.0 9 10 \n", "\n", " polsoc ptheo preg compp game wpol male \n", "id \n", "М141БПЛТЛ008 9 8.0 5 5.0 10 4 1 " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df[\"econ\"] == 9] # двойное равенство для равенства" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Можно формулировать сложные условия. Выберем студентов с оценкой по экономике от 6 до 8 (8 не включается)." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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первая закрывающая скобка не после 9" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Результат получился совсем неверным. Потому что Python понял наше условие не так, как нужно. Теперь выберем студентов с оценкой по политической истории ниже 5 или с оценкой по истории политических учений ниже 5:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " catps mstat soc econ eng polth mstat2 phist law phil \\\n", "id \n", "М141БПЛТЛ015 6 9 7 6 9 7 9 4.0 7 7 \n", "М141БПЛТЛ036 8 10 7 8 8 6 9 4.0 8 8 \n", "М141БПЛТЛ049 6 7 6 6 8 6 8 4.0 8 5 \n", "М141БПЛТЛ048 8 6 8 6 9 6 4 4.0 6 4 \n", "М141БПЛТЛ071 6 9 7 7 9 6 8 4.0 6 7 \n", "М141БПЛТЛ035 5 6 7 6 8 5 5 4.0 6 6 \n", "М141БПЛТЛ030 7 6 6 6 7 6 6 4.0 8 5 \n", "М141БПЛТЛ063 5 5 6 4 8 4 4 4.0 5 4 \n", "М141БПЛТЛ064 7 8 6 7 6 6 8 4.0 6 4 \n", "130232038 6 7 6 5 8 4 8 4.0 8 4 \n", "М141БПЛТЛ023 7 9 6 8 9 6 9 4.0 7 7 \n", "М141БПЛТЛ054 7 8 6 4 8 6 4 4.0 6 4 \n", "М141БПЛТЛ012 6 6 7 4 10 6 5 4.0 7 5 \n", "М141БПЛТЛ007 6 7 7 6 7 6 7 4.0 5 5 \n", "М141БПЛТЛ050 8 6 6 6 8 4 5 4.0 5 5 \n", "М141БПЛТЛ066 7 10 7 7 9 5 8 4.0 6 5 \n", "М141БПЛТЛ005 5 7 5 5 7 4 7 4.0 5 4 \n", "М141БПЛТЛ044 4 5 7 4 6 4 4 5.0 4 4 \n", "\n", " polsoc ptheo preg compp game wpol male \n", "id \n", "М141БПЛТЛ015 7 6.0 7 7.0 10 7 0 \n", "М141БПЛТЛ036 7 6.0 7 6.0 7 8 1 \n", "М141БПЛТЛ049 9 6.0 8 5.0 6 8 0 \n", "М141БПЛТЛ048 8 4.0 6 7.0 7 8 0 \n", "М141БПЛТЛ071 7 6.0 5 NaN 5 7 0 \n", "М141БПЛТЛ035 7 5.0 8 7.0 6 7 0 \n", "М141БПЛТЛ030 5 5.0 8 5.0 7 9 1 \n", "М141БПЛТЛ063 5 4.0 7 5.0 8 8 0 \n", "М141БПЛТЛ064 4 4.0 6 5.0 4 7 0 \n", "130232038 5 5.0 6 4.0 5 6 0 \n", "М141БПЛТЛ023 7 6.0 4 4.0 7 5 1 \n", "М141БПЛТЛ054 8 4.0 4 4.0 4 8 1 \n", "М141БПЛТЛ012 7 4.0 5 4.0 4 8 1 \n", "М141БПЛТЛ007 6 5.0 4 5.0 4 7 1 \n", "М141БПЛТЛ050 6 4.0 5 4.0 6 6 0 \n", "М141БПЛТЛ066 6 4.0 6 4.0 5 6 0 \n", "М141БПЛТЛ005 5 5.0 4 4.0 4 8 1 \n", "М141БПЛТЛ044 4 4.0 6 NaN 5 5 1 " ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[(df[\"phist\"] < 5) | (df[\"polth\"] < 5)] # оператор | для условия или " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Здесь наше выражение в квадратных скобках принимает значение *True*, когда хотя бы одно из условий верно: либо верно первое, либо второе, либо оба." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Добавление новых столбцов в таблице и удаление пропущенных значений" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Давайте добавим в нашу таблицу `df` новый столбец, который будет представлять собой среднюю оценку по социологии (посчитаем среднее арифметическое оценок по социологии и политической социологии). Чтобы добавить новый столбец, нужно указать его название в квадратных скобках:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "df[\"avg_Soc\"] = (df[\"soc\"] + df[\"polsoc\"]) / 2" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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catpsmstatsoceconengpolthmstat2phistlawphilpolsocptheopregcomppgamewpolmaleavg_Soc
id
М141БПЛТЛ024798898108.07997.088.061018.5
М141БПЛТЛ03181010101010109.0910109.088.0910110.0
М141БПЛТЛ0759991091098.091099.088.07919.0
М141БПЛТЛ017998899106.09998.088.08908.5
М141БПЛТЛ06910101010101098.081097.065.081019.5
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" ], "text/plain": [ " catps mstat soc econ eng polth mstat2 phist law phil \\\n", "id \n", "М141БПЛТЛ024 7 9 8 8 9 8 10 8.0 7 9 \n", "М141БПЛТЛ031 8 10 10 10 10 10 10 9.0 9 10 \n", "М141БПЛТЛ075 9 9 9 10 9 10 9 8.0 9 10 \n", "М141БПЛТЛ017 9 9 8 8 9 9 10 6.0 9 9 \n", "М141БПЛТЛ069 10 10 10 10 10 10 9 8.0 8 10 \n", "\n", " polsoc ptheo preg compp game wpol male avg_Soc \n", "id \n", "М141БПЛТЛ024 9 7.0 8 8.0 6 10 1 8.5 \n", "М141БПЛТЛ031 10 9.0 8 8.0 9 10 1 10.0 \n", "М141БПЛТЛ075 9 9.0 8 8.0 7 9 1 9.0 \n", "М141БПЛТЛ017 9 8.0 8 8.0 8 9 0 8.5 \n", "М141БПЛТЛ069 9 7.0 6 5.0 8 10 1 9.5 " ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Теперь внесем изменения в уже существующий столбец в таблице. В самом начале мы заметили, что некоторые столбцы имеют тип `float` (числа с плавающей точкой), а не `integer` (целые числа). Давайте попробуем сделать столбец с политической историей целочисленным." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "cannot convert float NaN to integer", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnewh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"phist\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnewh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"phist\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mValueError\u001b[0m: cannot convert float NaN to integer" ] } ], "source": [ "newh = [int(i) for i in df[\"phist\"]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Не получается! Почему? Python пишет, что не может превратить *NaN* в *integer*. Действительно, сложно превратить объект *Not a number* в целое число. Тип *float* относится к нему толерантно, а вот тип *integer* уже нет. Как быть? Давайте просто удалим из датафрейма все пропущенные значения (то есть строки, содержащие пропущенные значения). " ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "df = df.dropna() # удаляем и сохраняем изменения" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Теперь проделаем те же операции:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "newh = [int(i) for i in df[\"phist\"]]" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "df[\"phist\"] = newh" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Получилось!" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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catpsmstatsoceconengpolthmstat2phistlawphilpolsocptheopregcomppgamewpolmaleavg_Soc
id
М141БПЛТЛ0247988981087997.088.061018.5
М141БПЛТЛ03181010101010109910109.088.0910110.0
М141БПЛТЛ075999109109891099.088.07919.0
М141БПЛТЛ0179988991069998.088.08908.5
М141БПЛТЛ0691010101010109881097.065.081019.5
\n", "
" ], "text/plain": [ " catps mstat soc econ eng polth mstat2 phist law phil \\\n", "id \n", "М141БПЛТЛ024 7 9 8 8 9 8 10 8 7 9 \n", "М141БПЛТЛ031 8 10 10 10 10 10 10 9 9 10 \n", "М141БПЛТЛ075 9 9 9 10 9 10 9 8 9 10 \n", "М141БПЛТЛ017 9 9 8 8 9 9 10 6 9 9 \n", "М141БПЛТЛ069 10 10 10 10 10 10 9 8 8 10 \n", "\n", " polsoc ptheo preg compp game wpol male avg_Soc \n", "id \n", "М141БПЛТЛ024 9 7.0 8 8.0 6 10 1 8.5 \n", "М141БПЛТЛ031 10 9.0 8 8.0 9 10 1 10.0 \n", "М141БПЛТЛ075 9 9.0 8 8.0 7 9 1 9.0 \n", "М141БПЛТЛ017 9 8.0 8 8.0 8 9 0 8.5 \n", "М141БПЛТЛ069 9 7.0 6 5.0 8 10 1 9.5 " ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head() # Phist уже с целыми значениями" ] } ], "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": "3.12.4" } }, "nbformat": 4, "nbformat_minor": 4 }