{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Работа с таблицами. Введение в библиотеку pandas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Опять импортируем библиотеку:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "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": 3, "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": 3, "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": 4, "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": 4, "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": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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socpolsoc
id
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М141БПЛТЛ01789
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М141БПЛТЛ04088
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М141БПЛТЛ01577
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М141БПЛТЛ01898
М141БПЛТЛ03999
М141БПЛТЛ03677
М141БПЛТЛ04969
06114043109
М141БПЛТЛ04888
М141БПЛТЛ03476
М141БПЛТЛ04588
М141БПЛТЛ03388
М141БПЛТЛ08367
М141БПЛТЛ00889
М141БПЛТЛ00178
М141БПЛТЛ03868
М141БПЛТЛ05277
М141БПЛТЛ01187
М141БПЛТЛ00466
М141БПЛТЛ01078
М141БПЛТЛ07177
М141БПЛТЛ03577
М141БПЛТЛ03065
М141БПЛТЛ07065
М141БПЛТЛ05186
М141БПЛТЛ04677
М141БПЛТЛ04766
М141БПЛТЛ06365
М141БПЛТЛ02988
М141БПЛТЛ06464
М141БПЛТЛ07688
М141БПЛТЛ06276
М141БПЛТЛ07478
13023203865
М141БПЛТЛ02367
М141БПЛТЛ05468
М141БПЛТЛ01277
М141БПЛТЛ00667
М141БПЛТЛ05566
М141БПЛТЛ00776
М141БПЛТЛ05066
М141БПЛТЛ06676
М141БПЛТЛ04365
М141БПЛТЛ08484
М141БПЛТЛ00555
М141БПЛТЛ04474
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econengpolthmstat2phistlaw
id
М141БПЛТЛ024898108.07
М141БПЛТЛ031101010109.09
М141БПЛТЛ0751091098.09
М141БПЛТЛ017899106.09
М141БПЛТЛ06910101098.08
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" ], "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": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[:, 'econ' : 'law']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Выбор столбцов и строк таблицы" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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[:, 'econ' : 'law']`, 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": 7, "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": 7, "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": 8, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "cannot do slice indexing on with these indexers [mstat] of ", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'mstat'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;34m'econ'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32mC:\\Programs\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1470\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1471\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1472\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1473\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1474\u001b[0m \u001b[1;31m# we by definition only have the 0th axis\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mC:\\Programs\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m 2027\u001b[0m \u001b[1;32mcontinue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2028\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2029\u001b[1;33m \u001b[0mretval\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mretval\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2030\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2031\u001b[0m \u001b[1;31m# if the dim was reduced, then pass a lower-dim the next time\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mC:\\Programs\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 2078\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2079\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2080\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_slice_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2081\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2082\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mC:\\Programs\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_slice_axis\u001b[1;34m(self, slice_obj, axis)\u001b[0m\n\u001b[0;32m 2046\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2047\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2048\u001b[1;33m \u001b[0mslice_obj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_slice_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mslice_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2049\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mslice_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2050\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mslice_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'iloc'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", 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iloc\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1704\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mkind\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'iloc'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1705\u001b[1;33m return slice(self._validate_indexer('slice', key.start, kind),\n\u001b[0m\u001b[0;32m 1706\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'slice'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1707\u001b[0m self._validate_indexer('slice', key.step, kind))\n", "\u001b[1;32mC:\\Programs\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36m_validate_indexer\u001b[1;34m(self, form, key, kind)\u001b[0m\n\u001b[0;32m 4143\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4144\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mkind\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'iloc'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'getitem'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4145\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_invalid_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mform\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4146\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4147\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mC:\\Programs\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36m_invalid_indexer\u001b[1;34m(self, form, key)\u001b[0m\n\u001b[0;32m 1861\u001b[0m \"indexers [{key}] of {kind}\".format(\n\u001b[0;32m 1862\u001b[0m \u001b[0mform\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mform\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mklass\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1863\u001b[1;33m kind=type(key)))\n\u001b[0m\u001b[0;32m 1864\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1865\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mget_duplicates\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;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": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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mstatsoc
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1305103844
\n", "
" ], "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": 9, "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": 10, "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": 10, "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": 11, "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
\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", "\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": 11, "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": 12, "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": 12, "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": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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": 13, "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": 14, "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": 14, "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": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 15, "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": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "9" ] }, "execution_count": 16, "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": 17, "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": 17, "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 быстрее, 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": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "9" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iat[4, 6] # оценка на пересечении строки 4 и столбца 6" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "9" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.iloc[4, 6] # оценка на пересечении строки 8 и столбца 4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Убедимся, что все верно:" ] }, { "cell_type": "code", "execution_count": 20, "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
М141БПЛТЛ07379889898.08997.076.01091
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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", "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", "М141БПЛТЛ073 7 9 8 8 9 8 9 8.0 8 9 \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 \n", "М141БПЛТЛ073 9 7.0 7 6.0 10 9 1 " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(9)" ] }, { "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": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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catpsmstatsoceconengpolthmstat2phistlawphilpolsocptheopregcomppgamewpolmale
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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
М141БПЛТЛ03998988868.07696.078.0491
М141БПЛТЛ036810788694.08876.076.0781
М141БПЛТЛ04558878676.07786.086.0580
М141БПЛТЛ03359879797.07887.085.0780
М141БПЛТЛ0081088981098.091098.055.01041
М141БПЛТЛ05277778666.08675.086.0571
М141БПЛТЛ07169779684.06776.05NaN570
М141БПЛТЛ02968879567.06585.074.0570
М141БПЛТЛ06478676684.06444.065.0470
М141БПЛТЛ02379689694.07776.044.0751
М141БПЛТЛ066710779584.06564.064.0560
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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", "М141БПЛТЛ072 10 9 8 10 9 8 9 8.0 8 10 \n", "М141БПЛТЛ026 7 10 8 7 10 7 9 8.0 8 8 \n", "М141БПЛТЛ073 7 9 8 8 9 8 9 8.0 8 9 \n", "М141БПЛТЛ060 7 8 7 7 9 8 8 5.0 7 5 \n", "М141БПЛТЛ021 8 9 8 8 9 8 8 7.0 7 7 \n", "М141БПЛТЛ018 7 7 9 7 9 7 8 6.0 6 7 \n", "М141БПЛТЛ039 9 8 9 8 8 8 6 8.0 7 6 \n", "М141БПЛТЛ036 8 10 7 8 8 6 9 4.0 8 8 \n", "М141БПЛТЛ045 5 8 8 7 8 6 7 6.0 7 7 \n", "М141БПЛТЛ033 5 9 8 7 9 7 9 7.0 7 8 \n", "М141БПЛТЛ008 10 8 8 9 8 10 9 8.0 9 10 \n", "М141БПЛТЛ052 7 7 7 7 8 6 6 6.0 8 6 \n", "М141БПЛТЛ071 6 9 7 7 9 6 8 4.0 6 7 \n", "М141БПЛТЛ029 6 8 8 7 9 5 6 7.0 6 5 \n", "М141БПЛТЛ064 7 8 6 7 6 6 8 4.0 6 4 \n", "М141БПЛТЛ023 7 9 6 8 9 6 9 4.0 7 7 \n", "М141БПЛТЛ066 7 10 7 7 9 5 8 4.0 6 5 \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", "М141БПЛТЛ026 8 8.0 8 7.0 7 8 0 \n", "М141БПЛТЛ073 9 7.0 7 6.0 10 9 1 \n", "М141БПЛТЛ060 8 5.0 7 8.0 7 9 1 \n", "М141БПЛТЛ021 6 6.0 8 6.0 7 8 0 \n", "М141БПЛТЛ018 8 7.0 7 7.0 7 8 0 \n", "М141БПЛТЛ039 9 6.0 7 8.0 4 9 1 \n", "М141БПЛТЛ036 7 6.0 7 6.0 7 8 1 \n", "М141БПЛТЛ045 8 6.0 8 6.0 5 8 0 \n", "М141БПЛТЛ033 8 7.0 8 5.0 7 8 0 \n", "М141БПЛТЛ008 9 8.0 5 5.0 10 4 1 \n", "М141БПЛТЛ052 7 5.0 8 6.0 5 7 1 \n", "М141БПЛТЛ071 7 6.0 5 NaN 5 7 0 \n", "М141БПЛТЛ029 8 5.0 7 4.0 5 7 0 \n", "М141БПЛТЛ064 4 4.0 6 5.0 4 7 0 \n", "М141БПЛТЛ023 7 6.0 4 4.0 7 5 1 \n", "М141БПЛТЛ066 6 4.0 6 4.0 5 6 0 " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df[\"econ\"]>6]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Почему нельзя было написать проще, то есть `df[\"Econ\"]>6`? Давайте напишем, и посмотрим, что получится:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id\n", "М141БПЛТЛ024 True\n", "М141БПЛТЛ031 True\n", "М141БПЛТЛ075 True\n", "М141БПЛТЛ017 True\n", "М141БПЛТЛ069 True\n", "М141БПЛТЛ072 True\n", "М141БПЛТЛ020 False\n", "М141БПЛТЛ026 True\n", "М141БПЛТЛ073 True\n", "М141БПЛТЛ078 False\n", "М141БПЛТЛ060 True\n", "М141БПЛТЛ040 False\n", "М141БПЛТЛ065 False\n", "М141БПЛТЛ053 False\n", "М141БПЛТЛ015 False\n", "М141БПЛТЛ021 True\n", "М141БПЛТЛ018 True\n", "М141БПЛТЛ039 True\n", "М141БПЛТЛ036 True\n", "М141БПЛТЛ049 False\n", "06114043 False\n", "М141БПЛТЛ048 False\n", "М141БПЛТЛ034 False\n", "М141БПЛТЛ045 True\n", "М141БПЛТЛ033 True\n", "М141БПЛТЛ083 False\n", "М141БПЛТЛ008 True\n", "М141БПЛТЛ001 False\n", "М141БПЛТЛ038 False\n", "М141БПЛТЛ052 True\n", "М141БПЛТЛ011 False\n", "М141БПЛТЛ004 False\n", "М141БПЛТЛ010 False\n", "М141БПЛТЛ071 True\n", "М141БПЛТЛ035 False\n", "М141БПЛТЛ030 False\n", "М141БПЛТЛ070 False\n", "М141БПЛТЛ051 False\n", "М141БПЛТЛ046 False\n", "М141БПЛТЛ047 False\n", "М141БПЛТЛ063 False\n", "М141БПЛТЛ029 True\n", "М141БПЛТЛ064 True\n", "М141БПЛТЛ076 False\n", "М141БПЛТЛ062 False\n", "М141БПЛТЛ074 False\n", "130232038 False\n", "М141БПЛТЛ023 True\n", "М141БПЛТЛ054 False\n", "М141БПЛТЛ012 False\n", "М141БПЛТЛ006 False\n", "М141БПЛТЛ055 False\n", "М141БПЛТЛ007 False\n", "М141БПЛТЛ050 False\n", "М141БПЛТЛ066 True\n", "М141БПЛТЛ043 False\n", "М141БПЛТЛ084 False\n", "М141БПЛТЛ005 False\n", "М141БПЛТЛ044 False\n", "13051038 False\n", "Name: econ, dtype: bool" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"econ\"]>6" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Выбор столбцов и строк таблицы" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Мы увидели просто результат проверки условия, набор из *True* и *False*. Когда мы подставляем это выражение в квадратные скобки, Python выбирает из `df` те строки, где выражение принимает значение *True*.\n", "\n", "Все операторы проверки условий работают как обычно:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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catpsmstatsoceconengpolthmstat2phistlawphilpolsocptheopregcomppgamewpolmale
id
М141БПЛТЛ0081088981098.091098.055.01041
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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": 23, "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": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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catpsmstatsoceconengpolthmstat2phistlawphilpolsocptheopregcomppgamewpolmale
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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": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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М141БПЛТЛ066710779584.06564.064.0560
М141БПЛТЛ00557557474.05455.044.0481
М141БПЛТЛ04445746445.04444.06NaN551
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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": 27, "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": 28, "metadata": {}, "outputs": [], "source": [ "df[\"avg_Soc\"] = (df[\"soc\"] + df[\"polsoc\"]) / 2" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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catpsmstatsoceconengpolthmstat2phistlawphilpolsocptheopregcomppgamewpolmaleavg_Soc
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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": 29, "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": 30, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "cannot convert float NaN to integer", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnewh\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"phist\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m(.0)\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnewh\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"phist\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;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": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 54 entries, М141БПЛТЛ024 to М141БПЛТЛ005\n", "Data columns (total 18 columns):\n", "catps 54 non-null int64\n", "mstat 54 non-null int64\n", "soc 54 non-null int64\n", "econ 54 non-null int64\n", "eng 54 non-null int64\n", "polth 54 non-null int64\n", "mstat2 54 non-null int64\n", "phist 54 non-null int64\n", "law 54 non-null int64\n", "phil 54 non-null int64\n", "polsoc 54 non-null int64\n", "ptheo 54 non-null float64\n", "preg 54 non-null int64\n", "compp 54 non-null float64\n", "game 54 non-null int64\n", "wpol 54 non-null int64\n", "male 54 non-null int64\n", "avg_Soc 54 non-null float64\n", "dtypes: float64(3), int64(15)\n", "memory usage: 8.0+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "df = df.dropna() # удаляем и сохраняем изменения" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 54 entries, М141БПЛТЛ024 to М141БПЛТЛ005\n", "Data columns (total 18 columns):\n", "catps 54 non-null int64\n", "mstat 54 non-null int64\n", "soc 54 non-null int64\n", "econ 54 non-null int64\n", "eng 54 non-null int64\n", "polth 54 non-null int64\n", "mstat2 54 non-null int64\n", "phist 54 non-null int64\n", "law 54 non-null int64\n", "phil 54 non-null int64\n", "polsoc 54 non-null int64\n", "ptheo 54 non-null float64\n", "preg 54 non-null int64\n", "compp 54 non-null float64\n", "game 54 non-null int64\n", "wpol 54 non-null int64\n", "male 54 non-null int64\n", "avg_Soc 54 non-null float64\n", "dtypes: float64(3), int64(15)\n", "memory usage: 8.0+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Теперь проделаем те же операции:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "newh = [int(i) for i in df[\"phist\"]]" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "df[\"phist\"] = newh" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Получилось!" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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 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": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head() # Phist уже с целыми значениями" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Для подсчета некоторых статистик (количества, среднее, максимум, минимум) можно также использовать методы датафрейма:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "7 16\n", "6 14\n", "5 10\n", "8 7\n", "9 4\n", "10 3\n", "Name: catps, dtype: int64" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['catps'].value_counts()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6.814814814814815" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['catps'].mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Более подробно со списком методов датафрейма можно познакомиться в документации (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.5" } }, "nbformat": 4, "nbformat_minor": 2 }