diff --git a/Imercao03Aula04.ipynb b/Imercao03Aula04.ipynb new file mode 100644 index 0000000..1e0faba --- /dev/null +++ b/Imercao03Aula04.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Imercao03Aula04.ipynb","provenance":[],"authorship_tag":"ABX9TyPcwF8Lk5SqWH5Lg9OzOi5U"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"6umk0o5RH-Va"},"source":["###Aula 04"]},{"cell_type":"code","metadata":{"id":"bfc6FyayIBC0","executionInfo":{"status":"ok","timestamp":1620345631647,"user_tz":180,"elapsed":805,"user":{"displayName":"Lucas Alves","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GggnLgsIu7-n6jYQ_ljAdv3Gc4bgSyInJ-NQwATIFU=s64","userId":"05442089660626989204"}}},"source":["import pandas as pd \n","import seaborn as sns\n","import numpy as np\n","import matplotlib.pyplot as plt"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"id":"p3uHDnwiH1-o","executionInfo":{"status":"ok","timestamp":1620345638566,"user_tz":180,"elapsed":6243,"user":{"displayName":"Lucas Alves","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GggnLgsIu7-n6jYQ_ljAdv3Gc4bgSyInJ-NQwATIFU=s64","userId":"05442089660626989204"}}},"source":["url_dados = 'https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_experimentos.zip?raw=true'\n","dados = pd.read_csv(url_dados, compression = 'zip')"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":255},"id":"yXg_ZF0HH4O1","executionInfo":{"status":"ok","timestamp":1620345640788,"user_tz":180,"elapsed":774,"user":{"displayName":"Lucas Alves","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GggnLgsIu7-n6jYQ_ljAdv3Gc4bgSyInJ-NQwATIFU=s64","userId":"05442089660626989204"}},"outputId":"0cc2ae55-ace5-4890-d420-4fea17056f38"},"source":["dados.head()"],"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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5 rows × 877 columns

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5 rows × 209 columns

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idtratamentotempodosecompostog-0g-1g-2g-3g-4g-5g-6g-7g-8g-9g-10g-11g-12g-13g-14g-15g-16g-17g-18g-19g-20g-21g-22g-23g-24g-25g-26g-27g-28g-29g-30g-31g-32g-33g-34...c-62c-63c-64c-65c-66c-67c-68c-69c-70c-71c-72c-73c-74c-75c-76c-77c-78c-79c-80c-81c-82c-83c-84c-85c-86c-87c-88c-89c-90c-91c-92c-93c-94c-95c-96c-97c-98c-99n_moaativo_moa
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1id_000779bfccom_droga72D1df89a8e5a0.07430.40870.29910.06041.01900.52070.23410.3372-0.40470.8507-1.1520-0.4201-0.09580.45900.08030.22500.52930.2839-0.34940.28830.9449-0.1646-0.2657-0.33720.3135-0.43160.47730.2075-0.4216-0.1161-0.0499-0.26270.9959-0.24830.2655...0.39050.70990.29120.4151-0.2840-0.3104-0.63730.2887-0.07650.25390.44430.59320.20310.76390.5499-0.3322-0.09770.4329-0.27820.78270.59340.34020.14990.44200.93660.8193-0.42360.3192-0.42650.75430.47080.02300.29570.48990.15220.12410.60770.73710False
2id_000a6266acom_droga48D118bb41b2c0.62800.58171.5540-0.0764-0.03231.23900.17150.21550.00651.2300-0.4797-0.5631-0.0366-1.83000.6057-0.32780.6042-0.3075-0.1147-0.0570-0.0799-0.8181-1.53200.23070.49010.4780-1.39704.6240-0.04371.2870-1.85300.60690.42900.17830.0018...-0.04440.1894-0.0014-2.3640-0.46820.1210-0.5177-0.06040.1682-0.44360.49630.13630.33350.9760-0.0427-0.12350.09590.0690-0.9416-0.7548-0.1109-0.62720.30190.11720.1093-0.31130.3019-0.0873-0.7250-0.62970.61030.0223-1.3240-0.3174-0.6417-0.2187-1.40800.69319True
3id_0015fd391com_droga48D18c7f86626-0.5138-0.2491-0.26560.52884.0620-0.8095-1.95900.1792-0.1321-1.0600-0.8269-0.3584-0.8511-0.5844-2.56900.8183-0.0532-0.85540.1160-2.35202.1200-1.1580-0.7191-0.8004-1.4670-0.0107-0.89950.2406-0.2479-1.0890-0.75750.0881-2.73700.87450.5787...-2.3820-3.7350-2.9740-1.4930-1.6600-3.16600.2816-0.2990-1.1870-0.5044-1.7750-1.6120-0.9215-1.0810-3.0520-3.4470-2.7740-1.8460-0.5568-3.3960-2.9510-1.1550-3.2620-1.5390-2.4600-0.9417-1.55500.2431-2.0990-0.6441-5.6300-1.3780-0.8632-1.2880-1.6210-0.8784-0.3876-0.81540False
4id_001626bd3com_droga72D27cbed3131-0.3254-0.40090.97000.69191.4180-0.8244-0.2800-0.1498-0.87890.8630-0.2219-0.5121-0.95771.17500.20420.19700.1244-1.7090-0.3543-0.5160-0.3330-0.26850.76490.20571.37200.68350.8056-0.3754-1.20900.2965-0.07120.63890.6674-0.07831.1740...0.1535-0.4640-0.59430.39730.15000.51780.51590.60910.1813-0.42490.78320.65290.56480.48170.05870.53030.6376-0.3966-1.4950-0.9625-0.05410.62730.45630.06980.81340.19240.6054-0.18240.00420.00480.66701.06900.5523-0.30310.10940.2885-0.37860.71253True
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5 rows × 879 columns

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"],"text/plain":[" id tratamento tempo dose ... c-98 c-99 n_moa ativo_moa\n","0 id_000644bb2 com_droga 24 D1 ... 0.3801 0.4176 3 True\n","1 id_000779bfc com_droga 72 D1 ... 0.6077 0.7371 0 False\n","2 id_000a6266a com_droga 48 D1 ... -1.4080 0.6931 9 True\n","3 id_0015fd391 com_droga 48 D1 ... -0.3876 -0.8154 0 False\n","4 id_001626bd3 com_droga 72 D2 ... -0.3786 0.7125 3 True\n","\n","[5 rows x 879 columns]"]},"metadata":{"tags":[]},"execution_count":26}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZlYTyJ5ePfcB","executionInfo":{"status":"ok","timestamp":1620347982894,"user_tz":180,"elapsed":719,"user":{"displayName":"Lucas Alves","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GggnLgsIu7-n6jYQ_ljAdv3Gc4bgSyInJ-NQwATIFU=s64","userId":"05442089660626989204"}},"outputId":"b9043482-0ecd-4b62-81d4-230470d45c13"},"source":["dados_combinados.query('tratamento == \"com_controle\"')['ativo_moa'].value_counts()"],"execution_count":30,"outputs":[{"output_type":"execute_result","data":{"text/plain":["False 1866\n","Name: ativo_moa, dtype: int64"]},"metadata":{"tags":[]},"execution_count":30}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yBnAX7HkR3Oo","executionInfo":{"status":"ok","timestamp":1620348224123,"user_tz":180,"elapsed":747,"user":{"displayName":"Lucas Alves","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GggnLgsIu7-n6jYQ_ljAdv3Gc4bgSyInJ-NQwATIFU=s64","userId":"05442089660626989204"}},"outputId":"0fe90311-ea76-49c0-c946-a6ba488c5f53"},"source":["dados_combinados.query('tratamento == \"com_droga\"')['ativo_moa'].value_counts()"],"execution_count":31,"outputs":[{"output_type":"execute_result","data":{"text/plain":["True 14447\n","False 7501\n","Name: ativo_moa, dtype: int64"]},"metadata":{"tags":[]},"execution_count":31}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":516},"id":"ryKsmXFpSMR-","executionInfo":{"status":"ok","timestamp":1620348683788,"user_tz":180,"elapsed":1087,"user":{"displayName":"Lucas Alves","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GggnLgsIu7-n6jYQ_ljAdv3Gc4bgSyInJ-NQwATIFU=s64","userId":"05442089660626989204"}},"outputId":"66ae1fb1-3001-49b7-a66c-c6ab783f95bb"},"source":["composto_principal = dados_combinados['composto'].value_counts().index[:5]\n","plt.figure(figsize=(12,8))\n","sns.boxplot(data = dados_combinados.query('composto in @composto_principal'), y='g-0',x='composto',hue='ativo_moa')\n"],"execution_count":40,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":40},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"RO1sfVjpMvOf"},"source":["###Desafio 01: Encontrar o TOP 10 das ações do MOA (inibidor,agonista...)"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":363},"id":"TyCE31fIXhzE","executionInfo":{"status":"ok","timestamp":1620350242765,"user_tz":180,"elapsed":654,"user":{"displayName":"Lucas Alves","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GggnLgsIu7-n6jYQ_ljAdv3Gc4bgSyInJ-NQwATIFU=s64","userId":"05442089660626989204"}},"outputId":"9df2ca78-a558-4ccd-f08f-0015b1075f49"},"source":["#contagem_moa = dados_resultados.select_dtypes('int64').sum().sort_values(ascending=False)\n","#contagem_moa\n","\n","MOA = np.unique([col.split('_')[-1] for col in dados_resultados.drop('id',axis=1).columns])\n","freq = dados_resultados.drop(['id','n_moa','ativo_moa'],axis=1).sum()\n","contador = dict.fromkeys(MOA,[0])\n","for name in freq.index:\n"," contador[name.split('_')[-1]] += freq[name]\n","count = pd.DataFrame.from_dict(contador).T.rename({0:\"count\"},axis=1).sort_values(by='count', ascending=False)\n","count.head(10)"],"execution_count":46,"outputs":[{"output_type":"execute_result","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","
count
inhibitor9693
antagonist3449
agonist2330
blocker323
agent150
activator115
local80
antioxidant73
anti-inflammatory73
immunosuppressant73
\n","
"],"text/plain":[" count\n","inhibitor 9693\n","antagonist 3449\n","agonist 2330\n","blocker 323\n","agent 150\n","activator 115\n","local 80\n","antioxidant 73\n","anti-inflammatory 73\n","immunosuppressant 73"]},"metadata":{"tags":[]},"execution_count":46}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0u_77nL0YWkK","executionInfo":{"status":"ok","timestamp":1620349913045,"user_tz":180,"elapsed":630,"user":{"displayName":"Lucas Alves","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GggnLgsIu7-n6jYQ_ljAdv3Gc4bgSyInJ-NQwATIFU=s64","userId":"05442089660626989204"}},"outputId":"e07908a8-7658-42a6-8585-0b9e8a5c22b3"},"source":["contagem_moa"],"execution_count":43,"outputs":[{"output_type":"execute_result","data":{"text/plain":["n_moa 50532\n","nfkb_inhibitor 832\n","proteasome_inhibitor 726\n","cyclooxygenase_inhibitor 435\n","dopamine_receptor_antagonist 424\n"," ... \n","elastase_inhibitor 6\n","steroid 6\n","calcineurin_inhibitor 6\n","atp-sensitive_potassium_channel_antagonist 1\n","erbb2_inhibitor 1\n","Length: 207, dtype: int64"]},"metadata":{"tags":[]},"execution_count":43}]},{"cell_type":"markdown","metadata":{"id":"P2lX0Q8bRF0c"},"source":["###Desafio 02: Criar a coluna eh_controle para quando na linha tratamento == com_controle"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":255},"id":"CaeHmjYYZrQV","executionInfo":{"status":"ok","timestamp":1620350408886,"user_tz":180,"elapsed":617,"user":{"displayName":"Lucas Alves","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GggnLgsIu7-n6jYQ_ljAdv3Gc4bgSyInJ-NQwATIFU=s64","userId":"05442089660626989204"}},"outputId":"9364b8f0-d303-43c6-9567-bdd776008784"},"source":["dados_combinados['eh_controle'] = (dados_combinados['tratamento'] == 'com_controle')\n","dados_combinados.head()"],"execution_count":48,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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5 rows × 880 columns

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"],"text/plain":[" id tratamento tempo dose ... c-99 n_moa ativo_moa eh_controle\n","0 id_000644bb2 com_droga 24 D1 ... 0.4176 3 True False\n","1 id_000779bfc com_droga 72 D1 ... 0.7371 0 False False\n","2 id_000a6266a com_droga 48 D1 ... 0.6931 9 True False\n","3 id_0015fd391 com_droga 48 D1 ... -0.8154 0 False False\n","4 id_001626bd3 com_droga 72 D2 ... 0.7125 3 True False\n","\n","[5 rows x 880 columns]"]},"metadata":{"tags":[]},"execution_count":48}]},{"cell_type":"markdown","metadata":{"id":"hqQSFByyRb3f"},"source":["###Desafio 03: Criar três colunas para indicar o tempo 24, 48, 72"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":255},"id":"11_T8-p0aUEJ","executionInfo":{"status":"ok","timestamp":1620350497596,"user_tz":180,"elapsed":607,"user":{"displayName":"Lucas Alves","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GggnLgsIu7-n6jYQ_ljAdv3Gc4bgSyInJ-NQwATIFU=s64","userId":"05442089660626989204"}},"outputId":"c3d3dae5-5f89-424e-a977-7d943cd55ed7"},"source":["dados_combinados['tempo_24'] = (dados_combinados['tempo'] == 24)\n","dados_combinados['tempo_48'] = (dados_combinados['tempo'] == 48)\n","dados_combinados['tempo_72'] = (dados_combinados['tempo'] == 72)\n","dados_combinados.head()"],"execution_count":51,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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5 rows × 883 columns

\n","
"],"text/plain":[" id tratamento tempo ... tempo_24 tempo_48 tempo_72\n","0 id_000644bb2 com_droga 24 ... True False False\n","1 id_000779bfc com_droga 72 ... False False True\n","2 id_000a6266a com_droga 48 ... False True False\n","3 id_0015fd391 com_droga 48 ... False True False\n","4 id_001626bd3 com_droga 72 ... False False True\n","\n","[5 rows x 883 columns]"]},"metadata":{"tags":[]},"execution_count":51}]},{"cell_type":"markdown","metadata":{"id":"DYOr1dzpRp-Z"},"source":["###Desafio 04: Estudar obre combinações de DF"]},{"cell_type":"markdown","metadata":{"id":"0mYEgaIfUq-R"},"source":["###Desafio 05: Fazer análise mais detalhada considerando tempo e dose, para comparar as distribuições (Escolher uma droga e comparar com controle)"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":532},"id":"RksiwOy2atZN","executionInfo":{"status":"ok","timestamp":1620351031089,"user_tz":180,"elapsed":1283,"user":{"displayName":"Lucas Alves","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GggnLgsIu7-n6jYQ_ljAdv3Gc4bgSyInJ-NQwATIFU=s64","userId":"05442089660626989204"}},"outputId":"688e8963-c925-4fa0-8cf4-8d0d4d34cae3"},"source":["composto = dados_combinados[dados_combinados['composto']=='8c7f86626']\n","controle = dados_combinados[dados_combinados['tratamento']=='com_controle']\n","\n","fig, axs = plt.subplots(1,2,figsize=(20,8))\n","\n","sns.boxplot(data=composto,y='g-0', x='tempo', hue='dose',ax=axs[0])\n","axs[0].set_title('Composto 8c7f86626')\n","sns.boxplot(data=controle,y='g-0', x='tempo', hue='dose',ax=axs[1])\n","axs[1].set_title('Controle')"],"execution_count":67,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Controle')"]},"metadata":{"tags":[]},"execution_count":67},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"b7uMkYFbU1rb"},"source":["###Desafio 06: Descobrir se tem algum composto que dependendo da configuraçõa do experimento, ativa ou não ativa algum MOA"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"PgG1R4wAc2k5","executionInfo":{"status":"ok","timestamp":1620351318136,"user_tz":180,"elapsed":608,"user":{"displayName":"Lucas Alves","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GggnLgsIu7-n6jYQ_ljAdv3Gc4bgSyInJ-NQwATIFU=s64","userId":"05442089660626989204"}},"outputId":"71427ea6-2fee-429f-8237-69277000ec52"},"source":["dados_combinados[['composto','ativo_moa']].query('ativo_moa == True')"],"execution_count":69,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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compostoativo_moa
0b68db1d53True
218bb41b2cTrue
47cbed3131True
5e06749542True
78b87a7a83True
.........
23807a28556d51True
238086c3a459beTrue
23809df1d0a5a1True
23810ecf3b6b74True
238128b87a7a83True
\n","

14447 rows × 2 columns

\n","
"],"text/plain":[" composto ativo_moa\n","0 b68db1d53 True\n","2 18bb41b2c True\n","4 7cbed3131 True\n","5 e06749542 True\n","7 8b87a7a83 True\n","... ... ...\n","23807 a28556d51 True\n","23808 6c3a459be True\n","23809 df1d0a5a1 True\n","23810 ecf3b6b74 True\n","23812 8b87a7a83 True\n","\n","[14447 rows x 2 columns]"]},"metadata":{"tags":[]},"execution_count":69}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"7yCBhO3edw2k","executionInfo":{"status":"ok","timestamp":1620351334509,"user_tz":180,"elapsed":771,"user":{"displayName":"Lucas Alves","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GggnLgsIu7-n6jYQ_ljAdv3Gc4bgSyInJ-NQwATIFU=s64","userId":"05442089660626989204"}},"outputId":"a305a08e-14e7-44ab-eba6-bd8cd8be5374"},"source":["dados_combinados[['composto','ativo_moa']].query('ativo_moa == False')"],"execution_count":70,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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compostoativo_moa
1df89a8e5aFalse
38c7f86626False
6746ca1f5aFalse
8952b76dfcFalse
14e0cd5c091False
.........
2380124c787f29False
2380474515bfd2False
2380647dd8f190False
23811cacb2b860False
23813972f41291False
\n","

9367 rows × 2 columns

\n","
"],"text/plain":[" composto ativo_moa\n","1 df89a8e5a False\n","3 8c7f86626 False\n","6 746ca1f5a False\n","8 952b76dfc False\n","14 e0cd5c091 False\n","... ... ...\n","23801 24c787f29 False\n","23804 74515bfd2 False\n","23806 47dd8f190 False\n","23811 cacb2b860 False\n","23813 972f41291 False\n","\n","[9367 rows x 2 columns]"]},"metadata":{"tags":[]},"execution_count":70}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49},"id":"Wj06gM0feD3u","executionInfo":{"status":"ok","timestamp":1620351403551,"user_tz":180,"elapsed":620,"user":{"displayName":"Lucas Alves","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GggnLgsIu7-n6jYQ_ljAdv3Gc4bgSyInJ-NQwATIFU=s64","userId":"05442089660626989204"}},"outputId":"97dab671-eb44-42d1-c922-cd7a60feec58"},"source":["pd.merge(dados_combinados[['composto','ativo_moa']].query('ativo_moa == True'), \n"," dados_combinados[['composto','ativo_moa']].query('ativo_moa == False'), \n"," on = 'composto')"],"execution_count":71,"outputs":[{"output_type":"execute_result","data":{"text/html":["
\n","\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
compostoativo_moa_xativo_moa_y
\n","
"],"text/plain":["Empty DataFrame\n","Columns: [composto, ativo_moa_x, ativo_moa_y]\n","Index: []"]},"metadata":{"tags":[]},"execution_count":71}]},{"cell_type":"markdown","metadata":{"id":"-3kgmxB2VSTJ"},"source":["###Desafio 07: Descobrir se tem algum composto que dependendo da configuraçõa do experimento, ativa MOAs diferentes"]},{"cell_type":"markdown","metadata":{"id":"GCVh7clWVfrF"},"source":["###Desafio 08: Resumo do que voce aprendeu com os dados"]},{"cell_type":"markdown","metadata":{"id":"lg8_b4ffVyYl"},"source":["Analisamos a base de resultados e verificamos qual mecanismos de ação ele estava acionando."]},{"cell_type":"code","metadata":{"id":"lVIEhd1cM7OZ"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file