{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 07wk-1: Pandas – `lambda df:`의 활용, MultiIndex의 이해, tidydata의\n", "\n", "이해, `melt`/`stack`\n", "\n", "최규빈 \n", "2023-10-16\n", "\n", "\n", "\n", "# 1. 강의영상\n", "\n", "\n", "\n", "# 2. Imports" ], "id": "f0b99f27-a169-4bf6-ba77-3394fe3dcae6" }, { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [] }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from plotnine import *" ], "id": "c1b208d0-6da9-4a21-9cd5-e467a688e836" }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. Pandas – `lambda df:`의 활용\n", "\n", "## A. `lambda df:` with indexer\n", "\n", "`-` ref:\n", "\n", "\n", "`예시1` : 표현1,2,3은 같은 문법이다." ], "id": "d6a7643b-3905-47eb-ab09-d96021e05db1" }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [] }, "outputs": [], "source": [ "df = pd.DataFrame({'A':[-1,np.nan,1,1],'B':[2,3,np.nan,4],'C':[np.nan,4,5,6]})\n", "df" ], "id": "303fcd55-397a-4532-a8d1-f4c035738b24" }, { "cell_type": "markdown", "metadata": {}, "source": [ "(표현1)" ], "id": "1a63c742-9e99-4d52-ba33-29f44d73c488" }, { "cell_type": "code", "execution_count": 17, "metadata": { "tags": [] }, "outputs": [], "source": [ "df[df.A.isna()]" ], "id": "39660147-aa01-4a00-a72f-93dfa48fdf9e" }, { "cell_type": "markdown", "metadata": {}, "source": [ "(표현2)" ], "id": "860d5911-b629-48aa-8540-7d6f12b27542" }, { "cell_type": "code", "execution_count": 30, "metadata": { "tags": [] }, "outputs": [], "source": [ "df[(lambda _df: _df.A.isna())(df)]" ], "id": "74ee4218-3e2e-455c-8b2c-01baa89ed0d6" }, { "cell_type": "markdown", "metadata": {}, "source": [ "(표현3)" ], "id": "62e0c335-631a-4233-afc1-19906ec650b8" }, { "cell_type": "code", "execution_count": 69, "metadata": { "tags": [] }, "outputs": [], "source": [ "df[lambda _df: _df.A.isna()] " ], "id": "9f093c80-5cb4-448d-bed4-d66aa05137b4" }, { "cell_type": "markdown", "metadata": {}, "source": [ "`#`\n", "\n", "`예시2`: 예시1의 (표현3)은 `.loc`, `.iloc` 에서도 가능함" ], "id": "92699156-c895-4c7d-8a2b-95200465c837" }, { "cell_type": "code", "execution_count": 32, "metadata": { "tags": [] }, "outputs": [], "source": [ "df = pd.DataFrame({'A':[-1,np.nan,1,1],'B':[2,3,np.nan,4],'C':[np.nan,4,5,6]})\n", "df" ], "id": "d3b4124d-bf9a-49c0-825c-1661bdbfa73a" }, { "cell_type": "code", "execution_count": 71, "metadata": { "tags": [] }, "outputs": [], "source": [ "df.loc[lambda _df: _df.A.isna(), :]" ], "id": "fe8ad188-59fb-4155-9f9a-9278ad3e0bbb" }, { "cell_type": "code", "execution_count": 42, "metadata": { "tags": [] }, "outputs": [], "source": [ "df.iloc[lambda _df: list(_df.A.isna()), :]" ], "id": "841e56f1-2c6f-41ed-979c-c4b896679a0a" }, { "cell_type": "markdown", "metadata": {}, "source": [ "- iloc은 True, False 형태로 이루어진 pd.Series가 들어올 경우 인덱싱이\n", " 불가능하므로 리스트로 바꿔줘야했었음..\n", "\n", "`#`\n", "\n", "`예시3`: 왜 이런 문법이 있을까? 연속적으로 dataFrame을 변화시킬 경우\n", "유리한 테크닉" ], "id": "5e1b96a1-856d-4ce8-a798-55831ff2d0c3" }, { "cell_type": "code", "execution_count": 43, "metadata": { "tags": [] }, "outputs": [], "source": [ "df = pd.DataFrame({'A':[-1,np.nan,1,1],'B':[2,3,np.nan,4],'C':[np.nan,4,5,6]})\n", "df" ], "id": "9872d960-43d1-4f4f-8714-dc48b3c6453d" }, { "cell_type": "markdown", "metadata": {}, "source": [ "step1: D=A+B+C를 계산" ], "id": "a4be1684-9173-45e0-b8eb-cf2c09d8ec94" }, { "cell_type": "code", "execution_count": 59, "metadata": { "tags": [] }, "outputs": [], "source": [ "df.assign(D=df.A+df.B+df.C)" ], "id": "955c9690-09e1-4766-b4c1-67fd1ea948c7" }, { "cell_type": "markdown", "metadata": {}, "source": [ "step2: 여기에서 결측치의 값이 50%가 넘는 열만 고르고 싶다면?" ], "id": "3c9a8490-2edd-40f9-80c1-eaef4e7debcd" }, { "cell_type": "code", "execution_count": 62, "metadata": { "tags": [] }, "outputs": [], "source": [ "df.assign(D=df.A+df.B+df.C).loc[:,lambda _df: _df.isna().mean()>0.5]" ], "id": "b548491a-43b6-4fd8-97d5-8d27364bca7f" }, { "cell_type": "markdown", "metadata": {}, "source": [ "`#`\n", "\n", "## B. `lambda df:` with assign\n", "\n", "`예시1`" ], "id": "23173814-04d7-4dc7-9ae0-b30b423c4ad3" }, { "cell_type": "code", "execution_count": 63, "metadata": { "tags": [] }, "outputs": [], "source": [ "df = pd.DataFrame({'A':[-1,np.nan,1,1],'B':[2,3,np.nan,4],'C':[np.nan,4,5,6]})\n", "df" ], "id": "eb86d23e-e573-4165-832c-99a4e3a5d37b" }, { "cell_type": "markdown", "metadata": {}, "source": [ "step1: D=A+B+C" ], "id": "0d037315-207b-4d1e-907a-7d2142eb8947" }, { "cell_type": "code", "execution_count": 64, "metadata": { "tags": [] }, "outputs": [], "source": [ "df.eval('D=A+B+C')" ], "id": "a824f365-f811-41fd-af5c-86fece55a639" }, { "cell_type": "markdown", "metadata": {}, "source": [ "step2: 여기에서 결측치의 값을 row-wise하게 count하여 새로운열 `E`에\n", "할당하고 싶다면?" ], "id": "e0ad1b33-3803-4891-9192-9469c2afbd07" }, { "cell_type": "code", "execution_count": 77, "metadata": { "tags": [] }, "outputs": [], "source": [ "df.eval('D=A+B+C').assign(E = lambda _df: _df.isna().sum(axis=1))" ], "id": "be2fd32c-a18d-48fa-8233-4937d06f2e1b" }, { "cell_type": "markdown", "metadata": {}, "source": [ "`#`\n", "\n", "`예시2` – 원본데이터를 손상시키지 않으며 데이터를 변형하고 싶을때.." ], "id": "66fc1d08-7a79-4e2c-ae94-614f5ea024af" }, { "cell_type": "code", "execution_count": 78, "metadata": { "tags": [] }, "outputs": [], "source": [ "np.random.seed(43052)\n", "df = pd.DataFrame({'A':[12,234,3456,12345,654222]})\n", "df" ], "id": "69d1f1bf-3c7c-487f-a8c2-98849a1cd081" }, { "cell_type": "markdown", "metadata": {}, "source": [ "(풀이1) – 복사본생성 (실패)\n", "\n", "step1: 복사본생성 (?)" ], "id": "af3bd8bd-10da-41a3-9c30-b1727eac1c19" }, { "cell_type": "code", "execution_count": 80, "metadata": { "tags": [] }, "outputs": [], "source": [ "df2 = df " ], "id": "35177641-2e47-4ec7-aa78-7601d5665ca2" }, { "cell_type": "code", "execution_count": 82, "metadata": { "tags": [] }, "outputs": [], "source": [ "df2" ], "id": "e20cb000-6c8f-4e7f-9c03-35db039f84c5" }, { "cell_type": "markdown", "metadata": {}, "source": [ "step2: B=log(A), C=(B-B.mean())/B.std()" ], "id": "e17f053b-d72d-4e03-bafd-2ecc0fc73ad8" }, { "cell_type": "code", "execution_count": 85, "metadata": { "tags": [] }, "outputs": [], "source": [ "df2['B'] = np.log(df2['A'])" ], "id": "fe4335be-ce7d-42fe-a720-da33ea5f3088" }, { "cell_type": "code", "execution_count": 86, "metadata": { "tags": [] }, "outputs": [], "source": [ "df2" ], "id": "9b06ec96-93b4-4f5f-93fd-fb2cd8b12ecb" }, { "cell_type": "code", "execution_count": 90, "metadata": { "tags": [] }, "outputs": [], "source": [ "df2['C']=(df2['B']-df2['B'].mean())/df2['B'].std()\n", "df2" ], "id": "cf1b4ac3-5379-4b5a-9ce2-34db409b5645" }, { "cell_type": "code", "execution_count": 92, "metadata": { "tags": [] }, "outputs": [], "source": [ "df # 이게 왜 여기서 나오지?" ], "id": "104ecf1b-1f2e-4588-9148-952ee79ac93c" }, { "cell_type": "markdown", "metadata": {}, "source": [ "(풀이2) – 복사본생성 (성공)" ], "id": "329be732-975d-4116-9a70-4a5a2eaeb395" }, { "cell_type": "code", "execution_count": 93, "metadata": { "tags": [] }, "outputs": [], "source": [ "np.random.seed(43052)\n", "df = pd.DataFrame({'A':[12,234,3456,12345,654222]})\n", "df" ], "id": "ba505f0e-55d8-46a6-b42a-34b674c83525" }, { "cell_type": "markdown", "metadata": {}, "source": [ "step1: 복사본생성" ], "id": "0602f9ef-2938-4203-9350-aef27a731c36" }, { "cell_type": "code", "execution_count": 94, "metadata": { "tags": [] }, "outputs": [], "source": [ "df2 = df.copy()" ], "id": "60730879-f520-41ed-a5e6-f8e746f3b502" }, { "cell_type": "code", "execution_count": 95, "metadata": { "tags": [] }, "outputs": [], "source": [ "df2" ], "id": "56255957-2e4f-49e1-bf6a-b4b31a4f5548" }, { "cell_type": "markdown", "metadata": {}, "source": [ "step2: B=log(A), C=(B-B.mean())/B.std()" ], "id": "cb8d6ecd-6df5-447c-88b2-9a570eedfd15" }, { "cell_type": "code", "execution_count": 96, "metadata": { "tags": [] }, "outputs": [], "source": [ "df2['B'] = np.log(df2['A'])" ], "id": "ffd2375b-2048-4e07-83b2-67132f19a6b4" }, { "cell_type": "code", "execution_count": 97, "metadata": { "tags": [] }, "outputs": [], "source": [ "df2" ], "id": "4e51faae-1441-4a7c-a53e-97656ba00493" }, { "cell_type": "code", "execution_count": 98, "metadata": { "tags": [] }, "outputs": [], "source": [ "df2['C']=(df2['B']-df2['B'].mean())/df2['B'].std()\n", "df2" ], "id": "01d1cdcd-2557-4fbb-a55b-7130e6432ac4" }, { "cell_type": "code", "execution_count": 99, "metadata": { "tags": [] }, "outputs": [], "source": [ "df # 이게 왜 여기서 나오지?" ], "id": "56682bb5-e138-4a93-8c58-7cc0d585f69e" }, { "cell_type": "markdown", "metadata": {}, "source": [ "(풀이3) – `assign` + `lambda df:` 이용" ], "id": "0d900252-db3b-49e4-b75e-334d0776d385" }, { "cell_type": "code", "execution_count": 100, "metadata": { "tags": [] }, "outputs": [], "source": [ "np.random.seed(43052)\n", "df = pd.DataFrame({'A':[12,234,3456,12345,654222]})\n", "df" ], "id": "94fc1262-70ff-46a1-b093-f283d9e83ae3" }, { "cell_type": "code", "execution_count": 104, "metadata": { "tags": [] }, "outputs": [], "source": [ "df.assign(B=np.log(df.A)).assign(C= lambda df: (df.B-df.B.mean())/df.B.std())" ], "id": "7a08b9da-74c7-46ae-85f2-2bdade380bd4" }, { "cell_type": "markdown", "metadata": {}, "source": [ "(풀이4) – `eval` 이용" ], "id": "1f74418e-d1d7-4564-bf83-043d3386a335" }, { "cell_type": "code", "execution_count": 105, "metadata": { "tags": [] }, "outputs": [], "source": [ "np.random.seed(43052)\n", "df = pd.DataFrame({'A':[12,234,3456,12345,654222]})\n", "df" ], "id": "12ed3f66-cf7a-4031-a9f5-83fa96f52e43" }, { "cell_type": "code", "execution_count": 108, "metadata": { "tags": [] }, "outputs": [], "source": [ "df.eval('B= log(A)').eval('C= (B-B.mean())/(B.std())')" ], "id": "00d6f27c-0d1b-4139-8ed7-a23943fe2bb8" }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note: eval이 가장 우수해보인다. 그렇지만 eval expression에 지원하는\n", "함수는 한계가 있다. (sin,cos은 지원되는데 tan은 안된다든가..)" ], "id": "524ff5d2-ac04-4d7c-88e5-6804c544ed54" }, { "cell_type": "code", "execution_count": 114, "metadata": { "tags": [] }, "outputs": [], "source": [ "_df = pd.DataFrame({'A':np.linspace(-1.5,1.5,100)})\n", "_df" ], "id": "d64e8373-fac1-4d94-8c37-1881d78d3e0d" }, { "cell_type": "code", "execution_count": 117, "metadata": { "tags": [] }, "outputs": [ { "output_type": "display_data", "metadata": {}, "data": { "image/png": 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ty8CXcHJS7Zqel6qpOSmakpOiqbkpmpiVrIS46BBXDQAINcIKQqKr26sPKpu1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\nYII=\n" } } ], "source": [ "f = np.tan \n", "_df.eval('B=@f(A)').plot(y='B') # 그래서 이게 정석임.." ], "id": "ea00867a-6689-4b84-ab54-0bdb06e8bd7f" }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4. Pandas – MultiIndex의 이해\n", "\n", "## A. 원래 `df`, `s`는 딕셔너리 계열임\n", "\n", "`-` 예시1: df는 dct에서 만들수 있음" ], "id": "d0950eff-dcbb-4d74-8217-ea49f8f8c611" }, { "cell_type": "code", "execution_count": 118, "metadata": { "tags": [] }, "outputs": [], "source": [ "dct = {'A': [1,2,3],'B': [2,3,4]}\n", "df = pd.DataFrame(dct)\n", "df" ], "id": "41910082-1528-4b69-9b5d-2fab6bf26d15" }, { "cell_type": "code", "execution_count": 125, "metadata": { "tags": [] }, "outputs": [], "source": [ "df['A']" ], "id": "f17119d3-5e1a-4e21-a441-6e184a2f2ec3" }, { "cell_type": "markdown", "metadata": {}, "source": [ "`-` 예시2: s도 dct에서 만들수 있음" ], "id": "78cff983-761b-44be-a2b5-0610db346f18" }, { "cell_type": "code", "execution_count": 138, "metadata": { "tags": [] }, "outputs": [], "source": [ "dct = {'43052': 80, '43053': 90, '43054': 50}\n", "s = pd.Series(dct)\n", "s" ], "id": "eecdfdf5-5ef2-49f5-b160-f51bb77f01e3" }, { "cell_type": "code", "execution_count": 139, "metadata": { "tags": [] }, "outputs": [], "source": [ "s['43052']" ], "id": "ee735613-8f3b-4011-9031-7530cf1026c1" }, { "cell_type": "markdown", "metadata": {}, "source": [ "`-` 예시3: dict의 키로 올수 있는것들?\n", "\n", "- ref:\n", " \n", "\n", "튜플로 dct를 만든다면?" ], "id": "670b56d4-592b-45b1-85f2-f890f145ac9f" }, { "cell_type": "code", "execution_count": 154, "metadata": { "tags": [] }, "outputs": [], "source": [ "dct = {('43052',4): 80, ('43053',1): 90, ('43054',2): 50} # (학번,학년)\n", "s = pd.Series(dct)\n", "s" ], "id": "a88756a0-b1d1-4f93-bf87-9b3fd12b0aed" }, { "cell_type": "code", "execution_count": 157, "metadata": { "tags": [] }, "outputs": [], "source": [ "dct[('43052',4)]" ], "id": "2ad74ebb-3164-4b57-bbbc-b13e6603b54d" }, { "cell_type": "code", "execution_count": 158, "metadata": { "tags": [] }, "outputs": [], "source": [ "s[('43052',4)]" ], "id": "833a5bc3-d017-4823-a9c5-2a022e8a8a00" }, { "cell_type": "code", "execution_count": 159, "metadata": { "tags": [] }, "outputs": [], "source": [ "s.index" ], "id": "6c053fb3-8e43-430b-bcb5-d49a5695c104" }, { "cell_type": "markdown", "metadata": {}, "source": [ "## B. `.index` 혹은 `.columns`에 `name`이 있는 경우\n", "\n", "`예시1`: index에 이름이 있는 경우 `['id']`" ], "id": "08bd88ba-e232-45d7-957f-9e407b8565e2" }, { "cell_type": "code", "execution_count": 160, "metadata": { "tags": [] }, "outputs": [], "source": [ "dct = {'43052': 80, '43053': 90, '43054': 50}\n", "s = pd.Series(dct)\n", "s" ], "id": "194887ea-74ed-4abc-98e8-4bbab2eec6da" }, { "cell_type": "code", "execution_count": 161, "metadata": { "tags": [] }, "outputs": [], "source": [ "s.rename_axis(['id'])" ], "id": "2b027af2-d40d-4059-baa3-fe27077c4e7e" }, { "cell_type": "code", "execution_count": 457, "metadata": { "tags": [] }, "outputs": [], "source": [ "s.index, s.rename_axis(['id']).index," ], "id": "1f434bdb-4fb0-49d2-8481-933ca5bf2038" }, { "cell_type": "markdown", "metadata": {}, "source": [ "`#`\n", "\n", "`예시2`: index에 이름이 있는 경우 `['id','year']`" ], "id": "d857ed65-3432-41f1-bc3b-85142967f6db" }, { "cell_type": "code", "execution_count": 169, "metadata": { "tags": [] }, "outputs": [], "source": [ "dct = {('43052',4): 80, ('43053',1): 90, ('43054',2): 50} # (학번,학년)\n", "s = pd.Series(dct)\n", "s.rename_axis(['id','year'])" ], "id": "940337b5-9ed2-43c2-9cd3-6922c646fa23" }, { "cell_type": "code", "execution_count": 171, "metadata": { "tags": [] }, "outputs": [], "source": [ "s.index" ], "id": "f53c5673-2658-4a3a-8a89-887ae35921bd" }, { "cell_type": "code", "execution_count": 172, "metadata": { "tags": [] }, "outputs": [], "source": [ "s.rename_axis(['id','year']).index" ], "id": "0658c642-f75c-476e-b04e-f92f3cb9d4cd" }, { "cell_type": "markdown", "metadata": {}, "source": [ "`#`" ], "id": "1bdcb286-b583-4943-973a-702d7ac834de" }, { "cell_type": "code", "execution_count": 173, "metadata": { "tags": [] }, "outputs": [], "source": [ "dct = {('43052',4): 80, ('43053',1): 90, ('43054',2): 50} # (학번,학년)\n", "s = pd.Series(dct)\n", "s.rename_axis(['id','year'])" ], "id": "124068fd-4f62-4f4c-af47-9a5c157a8bc6" }, { "cell_type": "markdown", "metadata": {}, "source": [ "`예시3`: 예시2가 데이터프레임이라면 이렇게 보인다" ], "id": "9e418c78-99b6-4c19-8d9a-1accdfcabec4" }, { "cell_type": "code", "execution_count": 175, "metadata": { "tags": [] }, "outputs": [], "source": [ "dct = {('43052',4): 80, ('43053',1): 90, ('43054',2): 50} # (학번,학년)\n", "s = pd.Series(dct)\n", "df = pd.DataFrame(s.rename_axis(['id','year']))\n", "df" ], "id": "f836b28b-2caa-4a1f-bfae-5c9724d365f0" }, { "cell_type": "code", "execution_count": 461, "metadata": { "tags": [] }, "outputs": [], "source": [ "df.columns" ], "id": "1dca11d2-aa50-4d90-8ed5-bff9c56a77be" }, { "cell_type": "code", "execution_count": 462, "metadata": { "tags": [] }, "outputs": [], "source": [ "df.index" ], "id": "8a11434f-9f1c-4f1a-a6f2-3302a03f44b9" }, { "cell_type": "markdown", "metadata": {}, "source": [ "`#`\n", "\n", "`예시4`: 심슨의 역설 – 전체" ], "id": "14457893-1cce-49d3-9581-b428a83d88dd" }, { "cell_type": "code", "execution_count": 183, "metadata": { "tags": [] }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/tmp/ipykernel_2417284/2316601964.py:1: FutureWarning: The provided callable is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead." ] } ], "source": [ "df=pd.read_csv(\"https://raw.githubusercontent.com/guebin/DV2022/master/posts/Simpson.csv\",index_col=0,header=[0,1])\\\n", ".stack().stack().reset_index()\\\n", ".rename({'level_0':'department','level_1':'result','level_2':'gender',0:'count'},axis=1).pivot_table(index='gender', columns='result', values='count', aggfunc=sum)\n", "df" ], "id": "112de13a-48d5-4cbb-bdbc-61652d5ce528" }, { "cell_type": "code", "execution_count": 464, "metadata": { "tags": [] }, "outputs": [], "source": [ "df.index,df.columns" ], "id": "1004a7c4-704f-43c2-8494-2130f6f19ffa" }, { "cell_type": "markdown", "metadata": {}, "source": [ "1열과 2열을 더하고 싶다면? 단순히 아래와 같이 하면 된다. (여기에서\n", "`gender`,`result`는 각각 index 의 이름, columns의 이름일 뿐이므로 신경쓸\n", "필요 없음)" ], "id": "11d4b6ce-1ae9-4e56-88bb-f1835eaecf19" }, { "cell_type": "code", "execution_count": 195, "metadata": { "tags": [] }, "outputs": [], "source": [ "df['fail']+df['pass']" ], "id": "de2ffb77-d471-4c3e-a7dc-412ffcb059c6" }, { "cell_type": "markdown", "metadata": {}, "source": [ "`#`\n", "\n", "`예시5`: 심슨의 역설 – 학과별" ], "id": "e8fc6e0b-dd34-4433-baa0-a28f2cdddcb8" }, { "cell_type": "code", "execution_count": 196, "metadata": { "tags": [] }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/tmp/ipykernel_2417284/2434963071.py:1: FutureWarning: The provided callable is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead." ] } ], "source": [ "df = pd.read_csv(\"https://raw.githubusercontent.com/guebin/DV2022/master/posts/Simpson.csv\",index_col=0,header=[0,1])\\\n", ".stack().stack().reset_index()\\\n", ".rename({'level_0':'department','level_1':'result','level_2':'gender',0:'count'},axis=1).pivot_table(index=['gender','department'], columns='result',values='count',aggfunc=sum)\n", "df " ], "id": "c396f24f-d0da-459d-a46d-bd2265121877" }, { "cell_type": "code", "execution_count": 467, "metadata": { "tags": [] }, "outputs": [], "source": [ "df.index, df.columns" ], "id": "56e56bd5-1b69-4b1f-ad29-c013ed7ac868" }, { "cell_type": "markdown", "metadata": {}, "source": [ "학과별 합격률을 알고 싶다면?" ], "id": "0fa8910b-12c5-4f65-9666-91ea5b34aeaa" }, { "cell_type": "code", "execution_count": 205, "metadata": { "tags": [] }, "outputs": [], "source": [ "df.assign(rate = df['pass']/df.sum(axis=1))" ], "id": "2ef616bb-7abd-4dba-aab3-28a04c502ed1" }, { "cell_type": "markdown", "metadata": {}, "source": [ "`#`\n", "\n", "# 5. Pandas – tidydata\n", "\n", "## A. tidydata의 개념\n", "\n", "`-` 아래의 자료는 불리하다. (뭐가??)" ], "id": "7183178e-2d69-41c4-bea2-fc58cf1d83f8" }, { "cell_type": "code", "execution_count": 206, "metadata": { "tags": [] }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/tmp/ipykernel_2417284/2434963071.py:1: FutureWarning: The provided callable is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead." ] } ], "source": [ "df = pd.read_csv(\"https://raw.githubusercontent.com/guebin/DV2022/master/posts/Simpson.csv\",index_col=0,header=[0,1])\\\n", ".stack().stack().reset_index()\\\n", ".rename({'level_0':'department','level_1':'result','level_2':'gender',0:'count'},axis=1).pivot_table(index=['gender','department'], columns='result',values='count',aggfunc=sum)\n", "df " ], "id": "4ed68005-c1ca-4932-9431-26e19e2c7e58" }, { "cell_type": "markdown", "metadata": {}, "source": [ "`-` 가정1: 만약에 A학과에 해당하는 결과만 뽑고 싶다면? –\\> departmet가\n", "column으로 있어야함..\n", "\n", "`-` 가정2: 이 데이터를 바탕으로 합격한사람만 bar plot을 그리고 싶다면?\n", "–\\> department, gender, pass 가 column으로 있어야함.." ], "id": "308858b8-7a06-4848-a19c-3d9f4328f091" }, { "cell_type": "code", "execution_count": 479, "metadata": { "tags": [] }, "outputs": [ { "output_type": "display_data", "metadata": {}, "data": { "image/png": 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} } ], "source": [ "tidydata = df['pass'].reset_index()\n", "#---# \n", "fig = ggplot(tidydata)\n", "col = geom_col(aes(x='department',y='pass',fill='gender'),position='dodge')\n", "fig + col" ], "id": "ca4fb5ba-2fe6-4fbb-b627-d8357f6dd88e" }, { "cell_type": "markdown", "metadata": {}, "source": [ "`-` tidydata 정의: \n", "\n", "1. Each variable must have its own column.\n", "2. Each observation must have its own row.\n", "3. Each value must have its own cell.\n", "\n", "## B. tidydata가 아닌 예시\n", "\n", "`예시1` – MultiIndex 구조를 가지면 무조건 tidydata가 아니다." ], "id": "79bd436d-7174-44dc-a7f7-3fe3b231e8ce" }, { "cell_type": "code", "execution_count": 213, "metadata": { "tags": [] }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/tmp/ipykernel_2417284/2434963071.py:1: FutureWarning: The provided callable is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead." ] } ], "source": [ "df = pd.read_csv(\"https://raw.githubusercontent.com/guebin/DV2022/master/posts/Simpson.csv\",index_col=0,header=[0,1])\\\n", ".stack().stack().reset_index()\\\n", ".rename({'level_0':'department','level_1':'result','level_2':'gender',0:'count'},axis=1).pivot_table(index=['gender','department'], columns='result',values='count',aggfunc=sum)\n", "df " ], "id": "9436c25c-c5c1-4086-b945-6cd22aaa2606" }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 이건 tidydata가 아니고" ], "id": "29784cb4-aee6-4974-9070-3540ed82c94a" }, { "cell_type": "code", "execution_count": 481, "metadata": { "tags": [] }, "outputs": [], "source": [ "tidydata = df.stack().reset_index().rename({0:'applicant_count'},axis=1)\n", "tidydata " ], "id": "3bcf27aa-371b-4477-bb8c-ee23d111452b" }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 이것이 tidydata\n", "\n", "`-` 구분하는 방법1: 직관에 의한 설명\n", "\n", "- query쓰기 불편: 남성지원자만 뽑고 싶다면?, 학과A만 뽑고싶다면?\n", " 탈락한지원자만 뽑고싶다면? 학과A에서 탈락한 지원자만 뽑고싶다면??\n", "- 시각화하기 불편:\n", "- 하여튼 다루기 불편해..\n", "\n", "`-` 구분하는 방법2: 정의에 의한 설명\n", "\n", "- `df`는 원칙 1에 위배된다. (왜냐하면 `gender`,\n", " `department`,`result`,`applicant_count`에 해당하는 변수는 하나의\n", " 컬럼을 차지하지 못함)\n", "- `df`는 원칙 2에 위배된다. (왜냐하면 하나의 행에 2개의\n", " `applicant_count` observation이 존재함)\n", "\n", "`#`\n", "\n", "`예시2` – 아래의 자료는 tidydata가 아니다." ], "id": "b8748178-b2c3-42fa-9d69-de306e65eb69" }, { "cell_type": "code", "execution_count": 482, "metadata": { "tags": [] }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/tmp/ipykernel_2278789/2865855793.py:1: FutureWarning: The provided callable is currently using DataFrameGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"sum\" instead." ] } ], "source": [ "df=pd.read_csv(\"https://raw.githubusercontent.com/guebin/DV2022/master/posts/Simpson.csv\",index_col=0,header=[0,1])\\\n", ".stack().stack().reset_index()\\\n", ".rename({'level_0':'department','level_1':'result','level_2':'gender',0:'count'},axis=1).pivot_table(index='gender', columns='result', values='count', aggfunc=sum)\\\n", ".assign(pass_fail = lambda df: list(map(lambda x,y: (y,x),df['fail'],df['pass']))).drop(['fail','pass'],axis=1).reset_index()\n", "df" ], "id": "5762c324-54d3-442f-b21e-b9121471d30a" }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 이 `df`는 원칙 3에 위배된다.\n", "\n", "`#`\n", "\n", "`# 예시3` – wide df" ], "id": "dccea2d9-27ad-4d53-91dc-558fb5ae1ccf" }, { "cell_type": "code", "execution_count": 214, "metadata": { "tags": [] }, "outputs": [], "source": [ "df=pd.read_csv('https://raw.githubusercontent.com/guebin/2021DV/master/_notebooks/phone.csv')\n", "df" ], "id": "fceee743-6db9-4f73-9aef-aed333df025b" }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 이건 tidydata 가 아니고" ], "id": "e98f10f2-78c5-4602-9078-e7034e5f3111" }, { "cell_type": "code", "execution_count": 484, "metadata": { "tags": [] }, "outputs": [], "source": [ "tidydata = df.melt(id_vars='Date').assign(Date = lambda _df: _df.Date.apply(pd.to_datetime))\n", "tidydata" ], "id": "d48a8ba1-3fb5-48b6-95d3-9258bf37beb5" }, { "cell_type": "markdown", "metadata": {}, "source": [ "- 이건 tidydata 이다.\n", "\n", "`-` df를 가지고 아래와 같은 그림을 그릴 수 있겠어?" ], "id": "982c31eb-aa23-4912-ad49-eb1e1569eadc" }, { "cell_type": "code", "execution_count": 485, "metadata": { "tags": [] }, "outputs": [ { "output_type": "display_data", "metadata": {}, "data": { "image/png": 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9qoaAxc+GUvO8ioiINJBZs2axePFigBqDuV+r3G7s2LHbzdn30EMPUVJSUqWT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\nSABg5Bi4OuhyKvJrycW/UPrQP0l89zUARl4+wetuJnDOYAy3J8PVNX6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p2eHAf/6QJhGM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4BhgNdL1th3MX2+WMcqMf6l3xPc/AsAjq4XYm92VowTVR6G00nc9TeD3QHhMFkffkA4IyPWsYot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\nL79EioC1atU6YdsWLVrQsGFDdu3axU033cT48eM577zzCIfDfPvtt9xxx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hxOW6MT9s2h\nwL8EvzGow21NPZt8/y3k+28h5pbTYG+m3t6Co2pRxGmIb6EhvgVTyyU3OUU4YAy9Ng9MCCGEEEII\nccUkACiEEEL0AUp5NMbfoya2gqhbmlqu4SPPt4CCwG2XVc/PbwxiQPBuigJ30eIcoSG+mUZ7Jx5R\nHNVATewtamJv4deHEvbNIdc3G1PP7YqHJoQQQgghhLhKEgAUQgghejGlHOrtLdTE3sT2KlLLdS1I\nvu8W8v2LMPXsK96/pulkWePJssZTHPwITfFd1NubaXb2A4qYV8bZ6HLORl8gy5yYrBc4FV3zdcKj\nE0IIIYQQQnQGCQAKIYQQvZCnYtTZ66mJvoWj6lLLTS2XfP+t5Plv6vSmHbrmI9c3i1zfLByvngZ7\nK/X2ZmJeGaBodvbR7OxDJ0CObzq51mxC5lipFyiEEEIIIUQ3kwCgEEII0Yu4Xgu19hpqY6txVXNq\nuaUXUehfQq5vLrpmdfk4TD1MQeA2CgK3EXXLUvUCXdWAR5R6ewP19gZMLT85RXgOfqO4y8clhBBC\nCCGEaE8CgEIIIUQvEPfqqI2toi72Lh6x1HK/PpTCwO3kWNPRNKNbxhYwhhII3suAwN00OwdpsLfQ\nGN+JIo6jaqmOvUF17A0CxohEMNCaiXEV05KFEEIIIYQQl0cCgEIIIUQPZruV1MTepN7ehMJJLQ8a\noykMLCXLnIymad04wjaaZpBtTSLbmoSr/oJGeycN8c3JbsSKqHuCaOQEFZHnybYmE7bmkGVNviYZ\ni0IIIYQQlysnJ6e7hyBEp5EAoBBCCNEDRd1TVEdX0hjfDqjU8ixzEoWBpYTMsd03uEtgaAHy/PPI\n888j7tXQYG+h3t6cbFTi0hTfRVN8F7oWIteaSdg3m4AxqscEM4UQQgghhOhLJAAohBBC9CAtzlGq\noytodvamLdXIsaZT6F9KwBzWbWO7UpZeQGHgDgr8S4m6J2mwN9MQ34armvBUC3X2WurstVj6gOQU\n4dn4jKLuHrYQQgghhBB9hgQAhRBCiG6mVKKDbnV0BRH3aGq5hkmubw6F/tvxGQO6cYSdQ9M0guYI\nguYIBqp7aXL20WBvoSm+G4VD3KukKvoKVdFXCBpjCPvmkuObjqEFu3voQgghhBBC9GoSABRCCCG6\niVIejfHtVEdXEvPKUss1fOT7byLffyuWntd9A+xCmmaSY00lx5qK67XQEN9Og72ZiHsMgIh7lEjk\nKBWRP5BtTSXsm0OWObHbGp0IIYQQQgjRm0kAUAghhLjGPBWnwd5EdexN4l5VarmhZZHvX0S+72YM\nPasbR3htGXooGfC8CdutpN7eTEN8M3GvGoVDY3w7jfHtGFpOsl7gXPzGMKkXKIQQoldQykXh4Kk4\nijhKOSjiyZ8dVHK5l1ze0XqPePJ+6T+33f/c9aNzH0fX/N390IUQPYgEAIUQQohrxFVR6mLvUht7\nG0c1pJabWh4F/tvI8y/o92/WfcYABgTvpCjwQSLusUS9QHs7HhFc1UitvZpaezU+fXCiXqBvFpae\n393DFkII0YMp5V1y4CxzfRwvFaBz8JSdFrBrH9A79/6t+wOvGx5zHPr5e4rO0NjY2N1DuCzStVhc\niAQAhRBCiC7meE3UxlZTa7+Dp1pSy336QAr8txP2zUbT5CU5naZphMwxhMwxDAw+QFN8Nw32Zpqc\nfYCH7Z2hMvoildGXCJnjE/UCrWn9PoAqhBD9gadiNNjbOVtxGE9FcZVNPB45T2adg8Lp7iFfBQMd\nC00z0ZK3iZ8tNEw0zUJP3rat94GUzBBCnEM+bQghhBBdJO7VUhN9izp7PQo7tdxvlFDoX0qONQ1N\n07txhL2Drlnk+maQ65uB4zXSEN9Gg72ZqHsSULQ4B2lxDlKOjxzrBsK+OYTM8fK7FUKIPibqnKLO\nXkeDvQWP6DU6qtZB4M1CTwXkEoE4PS0Ad+H17e9//oCeKbVvhRCdRgKAQgghRCeLuRXUxFZSb28B\n3NTyoDGWosAdhMzrpH7dFTL1HAr8iyjwLyLmnknUC7S34Kg6FDYN8UT9QFMLk+ubnawXOLi7hy2E\nEOIKuSpKo72NOntd8sJPG59RSNBXjIaJ42gdZMJdLFPOusj9LUCX12whRJ8gAUAhhBCik0Sdk1TH\nVtIY3wmo1PJs83oKA0sJmqO7bWx9kd8YzMDgPQwIfIgW5zD19mYa4ztQ2DiqnprYm9TE3sRvlBC2\n5pDrm4mp53b3sIUQQlyEUoqoezKZ7bc1I4sedHKsaeT5FjB0wDxM08J1XWpra7ttvEII0RtIAFAI\nIYS4CkopWpzD1MRW0OwcSFujk2vNpDBwO35jSLeNrz/QNJ0sawJZ1gQ89REa4+/RYG+m2TkIKGJu\nKWfdUs5Gl5NlTiTsm0O2NQVd83X30IUQQqRxVYQGewt19npi7qmMdZY+gDzfAsK+uamLOVLqQQgh\nLp0EAIUQQogroJRHk7OH6ugKou7x1HINk7BvPgX+2/AZRd03wH5K1/yEfXMI++YQ9+posLfSYG8i\n5p0BPJqdvTQ7e9EJkOObQdg3h6AxWj5ECiFEN0lk+72fzPbbjiKeWqdhkmNNI+y7kZA5TqbiCiHE\nVZAAoBBCCHEZlHJpiG+jOroS2zuTWq4TIM+/kAL/Yplm2kNYeh6FgSUU+G8j5pZRH99Eg70VVzXi\nEaXeXk+9vR5LLyTXmk3YNwefMbC7hy2EEP2C6zVTH99CXWxdxuspgE8vJs+3gFzfXEw9u5tGKIQQ\nfYsEAIUQQohL4CmbensjNbG3iHvVqeWGlk2+fzH5voUYeqgbRyjOR9M0AuYwAuYwBgY+TLNzgHp7\nM03xXSjixL1qqmOvUx17nYAxkrBvLrnWDAw9q7uHLoQQfYpSioh7lLrYumTNVie1TsMix5pOnv/G\nZGa2ZPsJIURnkgCgEEIIcQGuilAXW0tNbBWuakwtN7V8CgNLCPvmSy25XkTTDLKtyWRbk3FVhEZ7\nJ/X2JiLuEQCi7nGikeNURJ4j27qe/MA8wmphN49aCCF6N8drot7eRL29HturyFjn1wcT9t9I2Joj\nF9KEEKILSQBQCCGE6IDjNVIbW0Vt7B08oqnlPn0QhYHbybVmoWlGN45QXC1DC5Lnn0+efz62W01D\nfAsN9mZs7yzg0hR/j6b4e5xp/h1h31zyfIuw9LzuHrYQQvQKSnm0OIeps9clM67Ts/185Ppmkudb\nQMAYKdl+QghxDUgAUAghhEhju9XUxN6i3t6QUYg8YIyg0L+UbGuKNIzog3xGIUXGMgr9dxB1T1Bv\nb6Ihvg1PteB4jVRH36Qmuppc3xwK/bfjMwZ095CFEKJHcrwG6u1N1NnriHtVGev8xjDyfDeS65uF\noQW7aYRCiHM1NDTw05/+lFdeeYX9+/dTXV1NTk4OI0aM4Pbbb+eRRx5hzJgx3T1McZUkACiEEEIA\nMfcM1dGVNMS3Al5qecicQKF/KSFzvGQo9AOaphE0RxI0R1Ks7qfF20+Tu5HalvdQOMnGIRvIsWZQ\nGLidgDGsu4cshBDdTikvWV91PY3xXaS/jur4yfXNIuxbQMAY3mmvpe6pKurW7MVXXIB/0vBO2acQ\n/dHy5ct59NFHqapKBOw1TSMvL4/6+nq2b9/O9u3b+cEPfsDXvvY1nnzySXk/3ItJAFAIIUS/FnGO\nUx1dQZOzK22pRrY1lUL/UoLmiG4bm+hemmaS67uBEfmLOV25i8qW12mM7wAUjfFtNMa3kWVOpjCw\nlJApV8WFEP1P3Kuj3t5Ivb0ho0EWJDLnE9l+M9E1f6cd02uMEH15E/b6faBUark+tBBrQgnmdcMw\nxw5B81mddkwh+qpf/OIXPPzww3iex/XXX8+3v/1t7rjjDoLBIJ7nsWnTJp555hleeOEFvv3tb3Pq\n1Cl+/vOfd/ewxRWSAKAQQoh+RylFi3OA6thKWpxDaWt0wr7ZFPhvx28M6rbxiZ4naJYwNOtvsN1K\nqmMrabA3o3BodvbS3LSXoDGGwsBSssxJcmVcCNGnJbL99lEXW0eTs5fMbL8Aub7Z5PluJGB2boa0\ncl1i7+wh+upmiNjt1ntl1cTKqom9vRNMHXPU4EQw8LoSjJIBaLqU7xAi3e7du/nsZz+L53ksW7aM\nF154Ab+/LViv6zrz589n+fLlPPXUU3zrW9/iv//7v5k/fz6PPPJIN45cXCl5FhRCCNHnKeURdxuI\nueU02Ns50fQ9Spv/IxX807DI993CmNwnGBz6Kwn+ifPyGQMYHPoYo3OfoMB/KxqJDtAR9yinmn/I\n8cZ/psHeilJuN49UCCE6V9yroTLyCkcbvsWp5h/R5OymNfgXNEYzKPgJxoa/w6DQX3R68C++7ySN\n//S/RP/0bir4Z90whmE/+BzD/ukR8v9iMeb4oWAmP946Hs7hMqJ/3kTT956j4Ws/p/mnrxNbuwe3\nsr5TxyZEb/XYY48RjUYpLi7md7/7XUbw71yPP/44S5cuBeCb3/wmsVgste6JJ55A0zQWLVoEwG9/\n+1sWLFhAOBwmNzeXm2++mT/96U/n3ffIkYlGQL/4xS+or6/ny1/+MmPGjCEQCDB48GA+9alP8f77\n71/wsVRXV/ONb3yDKVOmkJ2dTXZ2NlOmTOH//b//R01NzWX8Vvo2yQAUQgjRqyjl4qoWXNWEq5px\nvebEbevPqgnHa8ZTzTjJZZ6KQI1qty+dIPn+m8n3L8LUc7rh0YjeytLzGBi8j0L/HdTa71AbW42r\nmol5ZZxu+QWW/jKF/iXk+uaiazINTQjROynl0hTfQ529nmZnH5A25VYLEbbmkOe/Eb8xuEuO756t\nI/L8Opw9x9uOO6SQ4P03YU0YhpWfj2EYWCOKUQsnoew4ztEzOAdP4RwoxT2VqGmmWmLEdx4lv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dce54yqbe3kB19E0c1fY3MbQcCvyLyPPfjKEFr8lYLqS/nztKKeJeFS3OoUTQzzmckXl2Lp3A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\nBurq6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FQ/fxUTj4AwuC24hqsCi/l9/A22Jg9i4/JMYjuvJw9wZ3AtVwYWqXE3iZn1/dLR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\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dS6BiqaglcKy+uMnouliq5XJy/OUl8n9nKgv1fscYg6kWOrTca/y/GYZghpOSODGs9Kpx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\nQgghhFjB/n/Jkh0t2ySE4gAAAABJRU5ErkJggg==\n" } } ], "source": [ "fig = ggplot(tidydata)\n", "line = geom_line(aes(x='Date',y='value',color='variable'))\n", "fig + line" ], "id": "27f2b42c-0839-4c60-8e6d-ee26350e000e" }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 6. Pandas – `melt`/`stack`\n", "\n", "## A. `reset_index()`\n", "\n", "> 중첩구조를 가지는 series일 경우 `.reset_index()`를 사용하면 쉽게\n", "> tidydata를 얻을 수 있다.\n", "\n", "`-` 예시1" ], "id": "fb7cfbdb-f0e0-4c7d-8fea-effe4eb65302" }, { "cell_type": "code", "execution_count": 215, "metadata": { "tags": [] }, "outputs": [], "source": [ "dct = {'43052': 80, '43053': 90, '43054': 50}\n", "s = pd.Series(dct)\n", "s" ], "id": "dcb2e37b-624b-41a3-98fa-7532ba7124c5" }, { "cell_type": "code", "execution_count": 216, "metadata": { "tags": [] }, "outputs": [], "source": [ "s.reset_index()" ], "id": "26891a9f-31f4-415b-881b-8fa3c99a1e27" }, { "cell_type": "markdown", "metadata": {}, "source": [ "`-` 예시2" ], "id": "2ec21572-19ae-4c9e-9a89-9b48f9480ebf" }, { "cell_type": "code", "execution_count": 217, "metadata": { "tags": [] }, "outputs": [], "source": [ "dct = {('43052',4): 80, ('43053',1): 90, ('43054',2): 50} # (학번,학년)\n", "s = pd.Series(dct)\n", "s" ], "id": "ee0445a7-9b96-4c78-a344-0cd6c12f5b11" }, { "cell_type": "code", "execution_count": 220, "metadata": { "tags": [] }, "outputs": [], "source": [ "s.reset_index()" ], "id": "85add310-5588-4037-9a0c-dd84c501a211" }, { "cell_type": "markdown", "metadata": {}, "source": [ "`-` 예시3" ], "id": "fd3bcaef-bb08-412b-9a81-74ac095b3400" }, { "cell_type": "code", "execution_count": 221, "metadata": { "tags": [] }, "outputs": [], "source": [ "df=pd.read_csv(\"https://raw.githubusercontent.com/guebin/DV2022/master/posts/Simpson.csv\",index_col=0,header=[0,1]).stack().stack()\n", "df" ], "id": "955f6dda-9e11-46fa-b3e3-4cb3a9be8ad3" }, { "cell_type": "code", "execution_count": 223, "metadata": {}, "outputs": [], "source": [ "df.reset_index()" ], "id": "f3df23be-4cb8-4608-a1a7-6de0b5e4ad6f" }, { "cell_type": "markdown", "metadata": {}, "source": [ "`-` 예시4 – `.reset_index()` 는 말그대로 index를 reset 하는 명령어, 꼭\n", "`pd.Series`에만 쓰는건 아니다." ], "id": "b237a507-9e8e-4c3c-a92e-c4d5d89c8968" }, { "cell_type": "code", "execution_count": 224, "metadata": { "tags": [] }, "outputs": [], "source": [ "df=pd.read_csv(\"https://raw.githubusercontent.com/guebin/DV2022/master/posts/Simpson.csv\",index_col=0,header=[0,1]).stack()\n", "df" ], "id": "9487c16a-cb13-4a75-ae83-ee8649c7ea73" }, { "cell_type": "code", "execution_count": 225, "metadata": { "tags": [] }, "outputs": [], "source": [ "df.reset_index()" ], "id": "98638f54-7ce4-4cda-9893-32abf6b4eea7" }, { "cell_type": "markdown", "metadata": {}, "source": [ "## B. `melt()`\n", "\n", "`# 예시1`: 아래의 자료를 tidydata로 만들라." ], "id": "daf33ff4-14d3-4d6c-b0ce-8bf08d7a8992" }, { "cell_type": "code", "execution_count": 226, "metadata": { "tags": [] }, "outputs": [], "source": [ "df = pd.read_csv('https://raw.githubusercontent.com/guebin/2021DV/master/_notebooks/phone.csv')\n", "df" ], "id": "a52649df-a8ec-4879-b7dc-8bae706a8236" }, { "cell_type": "markdown", "metadata": {}, "source": [ "(풀이1) `.melt()` – 실패" ], "id": "f5984517-4a9e-4ed0-b2d5-d29a7c5f97b3" }, { "cell_type": "code", "execution_count": 237, "metadata": { "tags": [] }, "outputs": [], "source": [ "df.melt()" ], "id": "fc4b3ae1-804a-40c6-b460-cdee16a35ab9" }, { "cell_type": "markdown", "metadata": {}, "source": [ "(풀이2) `.melt(id_vars=)` – 성공" ], "id": "25b7f53f-9986-4c9e-83e1-95a84d70acb7" }, { "cell_type": "code", "execution_count": 238, "metadata": { "tags": [] }, "outputs": [], "source": [ "df.melt(id_vars='Date')" ], "id": "c039054f-2ca6-4d4c-bdbe-35327d54fb69" }, { "cell_type": "markdown", "metadata": {}, "source": [ "`#`\n", "\n", "## C. `stack()` + `reset_index()`\n", "\n", "> 제 최애테크닉: DataFrame을 MultiIndex를 가지는 Series로 “일부러”\n", "> 변환하고 reset_index()를 시킴\n", "\n", "`# 예시1`: 아래의 자료를 tidydata로 만들라." ], "id": "a720cd3c-231a-4421-88e1-7b5f8de73c2b" }, { "cell_type": "code", "execution_count": 239, "metadata": { "tags": [] }, "outputs": [], "source": [ "df = pd.read_csv('https://raw.githubusercontent.com/guebin/2021DV/master/_notebooks/phone.csv')\n", "df" ], "id": "33f3ce5f-7fb4-4cc4-aa5a-2407caca6292" }, { "cell_type": "code", "execution_count": 247, "metadata": { "tags": [] }, "outputs": [], "source": [ "df.set_index('Date').stack().reset_index()" ], "id": "c9896f21-973a-4f2a-a2e7-5080717bc8f4" }, { "cell_type": "markdown", "metadata": {}, "source": [ "`#`\n", "\n", "`# 예시2`: 아래의 자료를 tidydata로 만들어라." ], "id": "d614d778-805a-4c08-893f-dabad7b90f39" }, { "cell_type": "code", "execution_count": 248, "metadata": { "tags": [] }, "outputs": [], "source": [ "df = pd.read_csv(\"https://raw.githubusercontent.com/guebin/DV2022/master/posts/Simpson.csv\",index_col=0,header=[0,1])\n", "df" ], "id": "d0bf35b8-6c41-49ed-9ef2-74805d2fd0c9" }, { "cell_type": "code", "execution_count": 254, "metadata": { "tags": [] }, "outputs": [], "source": [ "df.stack().stack().reset_index()" ], "id": "b92a9597-5e53-4a85-be70-8771a2c6d31c" }, { "cell_type": "markdown", "metadata": {}, "source": [ "`#`\n", "\n", "## D. `unstack() + reset_index()`\n", "\n", "`# 예시1` – `.stack()`과 `.unstack()`은 반대연산" ], "id": "166d58c6-1e43-46c6-b6f0-1b79df858cbd" }, { "cell_type": "code", "execution_count": 270, "metadata": { "tags": [] }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/tmp/ipykernel_2417284/3693914824.py:2: FutureWarning: The provided callable is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"mean\" instead.\n", "/tmp/ipykernel_2417284/3693914824.py:2: FutureWarning: The provided callable is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"mean\" instead." ] } ], "source": [ "df=pd.read_csv('https://raw.githubusercontent.com/PacktPublishing/Pandas-Cookbook/master/data/flights.csv')\\\n", ".groupby([\"AIRLINE\",\"WEEKDAY\"]).agg({\"CANCELLED\":[np.mean,\"count\"],\"DIVERTED\":[np.mean,\"count\"]})\n", "df" ], "id": "8826b25c-ced0-4519-95d1-6a5d14e0fa72" }, { "cell_type": "code", "execution_count": 274, "metadata": { "tags": [] }, "outputs": [], "source": [ "df.stack().unstack()" ], "id": "e921f831-eced-45fb-a6c8-5c76658ae309" }, { "cell_type": "markdown", "metadata": {}, "source": [ "`#`\n", "\n", "`# 예시2` – 아래의 자료를 tidydata로 만들라." ], "id": "47d82698-ae38-4f9c-8d7f-4c3e98f793c5" }, { "cell_type": "code", "execution_count": 275, "metadata": { "tags": [] }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/tmp/ipykernel_2417284/3693914824.py:2: FutureWarning: The provided callable is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"mean\" instead.\n", "/tmp/ipykernel_2417284/3693914824.py:2: FutureWarning: The provided callable is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"mean\" instead." ] } ], "source": [ "df=pd.read_csv('https://raw.githubusercontent.com/PacktPublishing/Pandas-Cookbook/master/data/flights.csv')\\\n", ".groupby([\"AIRLINE\",\"WEEKDAY\"]).agg({\"CANCELLED\":[np.mean,\"count\"],\"DIVERTED\":[np.mean,\"count\"]})\n", "df" ], "id": "13fe6e29-abb1-4223-a54d-efe8abb6c8d7" }, { "cell_type": "markdown", "metadata": {}, "source": [ "(풀이1) – stack 2번" ], "id": "05f8d2cc-963b-4a9a-b330-716ae4dd20c6" }, { "cell_type": "code", "execution_count": 277, "metadata": { "tags": [] }, "outputs": [], "source": [ "df.stack().stack().reset_index()" ], "id": "7f2711ee-1adc-45ec-8c3b-fb4519978a2d" }, { "cell_type": "markdown", "metadata": {}, "source": [ "(풀이2) – unstack 2번" ], "id": "b71a1f28-25bb-40f7-9383-1579758c0bbf" }, { "cell_type": "code", "execution_count": 278, "metadata": { "tags": [] }, "outputs": [], "source": [ "df.unstack().unstack().reset_index()" ], "id": "8388fe61-8d27-4ce1-85c0-64b9b1c6bb49" }, { "cell_type": "markdown", "metadata": {}, "source": [ "`예시3` – 아래의 자료를 tidydata로 만들어라." ], "id": "0d8dabd5-0117-47db-adaf-d1d93811b16f" }, { "cell_type": "code", "execution_count": 279, "metadata": { "tags": [] }, "outputs": [], "source": [ "df=pd.read_csv(\"https://raw.githubusercontent.com/guebin/DV2022/master/posts/Simpson.csv\",index_col=0,header=[0,1])\n", "df" ], "id": "179f995c-c718-40f7-886e-f2d2a2e443f1" }, { "cell_type": "markdown", "metadata": {}, "source": [ "(풀이1) – stack 2번" ], "id": "b9e1c59a-7cc6-488c-9f20-7102529e102d" }, { "cell_type": "code", "execution_count": 282, "metadata": { "tags": [] }, "outputs": [], "source": [ "df.stack().stack().reset_index()" ], "id": "88be89ce-6bca-43bf-b2f7-e7c27072ebd7" }, { "cell_type": "markdown", "metadata": {}, "source": [ "(풀이2) – unstack 1번" ], "id": "64c7827f-e541-4e52-9069-a46107e06c48" }, { "cell_type": "code", "execution_count": 285, "metadata": { "tags": [] }, "outputs": [], "source": [ "df.unstack().reset_index()" ], "id": "35ac0da5-213f-41dd-a114-de890a8d7518" }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 7. HW\n", "\n", "아래의 자료를 tidydata로 변환하라." ], "id": "34208256-b844-40f3-844e-fd5f7a323d2e" }, { "cell_type": "code", "execution_count": 527, "metadata": { "tags": [] }, "outputs": [], "source": [ "s = pd.Series({\n", " ('female','A'):0.5,\n", " ('female','B'):0.2,\n", " ('male','A'):0.3,\n", " ('male','B'):0.7\n", "})\n", "s" ], "id": "60b2387b-2f84-4f09-8450-741a236394f5" }, { "cell_type": "code", "execution_count": 528, "metadata": { "tags": [] }, "outputs": [], "source": [ "# 출력결과는 아래와 같아야 한다. " ], "id": "2dd78b17-a915-4ad5-b003-85b255e7303f" } ], "nbformat": 4, "nbformat_minor": 5, "metadata": { "kernelspec": { "name": "python3", "display_name": "Python 3 (ipykernel)", "language": "python" }, "language_info": { "name": "python", "codemirror_mode": { "name": "ipython", "version": "3" }, "file_extension": ".py", "mimetype": "text/x-python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.13" } } }