├── .gitignore
├── LICENSE
├── README.md
├── data
├── .gitkeep
└── ipea-20250319T124834Z-001.zip
├── dia01
├── 01_hello_pandas.py
├── 02_series.py
├── 03_series_index.py
└── 04_dataframes.py
├── dia02
├── 01_csv.py
├── 02_clipboard.py
├── 03_html.py
├── 04_dataframes.py
└── 05_colunas.py
├── dia03
├── 01_filtro.py
├── 02_mais_filtros.py
└── 03_filtros_view.py
├── dia04
├── 01_novas_colunas.py
└── 02_sort.py
├── dia05
├── 01_conv_tipo.py
├── 02_na.py
└── 03_duplicatas.py
├── dia06
├── 01_apply.py
└── 02_ufs.py
├── dia07
├── 01_summary.py
├── 02_group.py
└── 03_custom_group.py
├── dia08
├── 01_merge.py
└── 02_concat.py
├── dia09
├── 01_db.py
├── 02_etl.py
├── etl.sql
├── pandas_sql.drawio
└── pandas_sql.png
└── exercicios
├── 02
└── 01.py
├── 03
└── exercicios.py
├── 04
├── drop_duplicates.py
└── exercicios.py
├── 05
└── exercicios.py
├── 06
├── 01.py
├── 02.py
├── 03.py
├── 04.py
├── 05.py
└── 06.py
├── case_cartao_credito
└── cartao.py
└── case_homicidios
├── 01_case.py
├── 02_stack_unstack.py
└── 03_pivot.py
/.gitignore:
--------------------------------------------------------------------------------
1 | *.csv
2 | *.xlsx
3 | *parquet
4 | *.db
--------------------------------------------------------------------------------
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537 | any implied license or other defenses to infringement that may
538 | otherwise be available to you under applicable patent law.
539 |
540 | 12. No Surrender of Others' Freedom.
541 |
542 | If conditions are imposed on you (whether by court order, agreement or
543 | otherwise) that contradict the conditions of this License, they do not
544 | excuse you from the conditions of this License. If you cannot convey a
545 | covered work so as to satisfy simultaneously your obligations under this
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548 | to collect a royalty for further conveying from those to whom you convey
549 | the Program, the only way you could satisfy both those terms and this
550 | License would be to refrain entirely from conveying the Program.
551 |
552 | 13. Use with the GNU Affero General Public License.
553 |
554 | Notwithstanding any other provision of this License, you have
555 | permission to link or combine any covered work with a work licensed
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562 |
563 | 14. Revised Versions of this License.
564 |
565 | The Free Software Foundation may publish revised and/or new versions of
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567 | be similar in spirit to the present version, but may differ in detail to
568 | address new problems or concerns.
569 |
570 | Each version is given a distinguishing version number. If the
571 | Program specifies that a certain numbered version of the GNU General
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573 | option of following the terms and conditions either of that numbered
574 | version or of any later version published by the Free Software
575 | Foundation. If the Program does not specify a version number of the
576 | GNU General Public License, you may choose any version ever published
577 | by the Free Software Foundation.
578 |
579 | If the Program specifies that a proxy can decide which future
580 | versions of the GNU General Public License can be used, that proxy's
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582 | to choose that version for the Program.
583 |
584 | Later license versions may give you additional or different
585 | permissions. However, no additional obligations are imposed on any
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587 | later version.
588 |
589 | 15. Disclaimer of Warranty.
590 |
591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
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612 | 17. Interpretation of Sections 15 and 16.
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620 |
621 | END OF TERMS AND CONDITIONS
622 |
623 | How to Apply These Terms to Your New Programs
624 |
625 | If you develop a new program, and you want it to be of the greatest
626 | possible use to the public, the best way to achieve this is to make it
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630 | to attach them to the start of each source file to most effectively
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642 | This program is distributed in the hope that it will be useful,
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649 |
650 | Also add information on how to contact you by electronic and paper mail.
651 |
652 | If the program does terminal interaction, make it output a short
653 | notice like this when it starts in an interactive mode:
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657 | This is free software, and you are welcome to redistribute it
658 | under certain conditions; type `show c' for details.
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660 | The hypothetical commands `show w' and `show c' should show the appropriate
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667 | .
668 |
669 | The GNU General Public License does not permit incorporating your program
670 | into proprietary programs. If your program is a subroutine library, you
671 | may consider it more useful to permit linking proprietary applications with
672 | the library. If this is what you want to do, use the GNU Lesser General
673 | Public License instead of this License. But first, please read
674 | .
675 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # Pandas 2025
2 |
3 | Curso renovado de Pandas realizado ao vivo de form gratuita, com gravação disponível no [YouTube](youtube.com/@teomewhy).
4 |
5 | ## Pré-requisito
6 |
7 | - Ter Python instalado. Sugestão de vídeo para instalação, [clique aqui](https://youtu.be/OeKzVjiiRm4?si=PT0v4LwNE9SUL-2m).
8 | - Python Básico: sintaxe da linguagem e lógica de programação. [Conheça mais aqui](https://www.youtube.com/playlist?list=PLvlkVRRKOYFSpRkqnR0p2A-eaVlpLnN3D).
9 |
10 | ## Material de apoio
11 |
12 | Temos um material de apoio para nosso treinamento, contendo algumas orientações e exercícios. [Confira aqui](https://docs.google.com/presentation/d/10_lCOieWozst3t2ldGaY78vxh4mOGkplHqXBQ7M3eDo/edit?usp=sharing).
13 |
14 | ## Dados
15 |
16 | Utilizaremos dados reais durante o nosso curso. Acesse os [dados aqui](https://www.kaggle.com/datasets/teocalvo/teomewhy-loyalty-system).
17 |
18 | ## Ementa
19 |
20 | 1. Principais objetos do Pandas
21 | - Series
22 | - Dataframes
23 |
24 | 2. Importando arquivos com pandas
25 | - csv; xlsx; parquet
26 |
27 | 3. Navegando pelos dados:
28 | - Informações básicas
29 | - Tipos de colunas
30 | - Navegação em linhas e colunas
31 | - Renomeando colunas
32 |
33 | 4. Filtrando dados
34 | - Condições lógicas
35 |
36 | 5. Transformações e remoções
37 | - Criação de novas colunas
38 | - Ordenação
39 | - Conversão de tipos
40 | - Aplicando funções em linhas e colunas
41 | - Removendo Duplicatas
42 | - Trabalhando com NAs
43 |
44 | 6. GroupBy
45 | - Agregando dados
46 | - O método agg
47 | - Agregações personalizadas
48 |
49 | 7. Cruzamento de dados
50 | - Merge
51 | - Concat
52 |
53 | 8. Manipulações adicionais
54 | - Stack
55 | - Unstack
56 | - Pivot Table
57 | - Explode
58 |
59 | 9. Conectando com Bancos SQL
60 | - Importando dados
61 | - Executando Queries
62 | - Escrevendo dados
63 |
64 |
65 |
--------------------------------------------------------------------------------
/data/.gitkeep:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/TeoMeWhy/pandas-2025/1f56a6f461ca014c36acdab69832e19e25216f30/data/.gitkeep
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/data/ipea-20250319T124834Z-001.zip:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/TeoMeWhy/pandas-2025/1f56a6f461ca014c36acdab69832e19e25216f30/data/ipea-20250319T124834Z-001.zip
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/dia01/01_hello_pandas.py:
--------------------------------------------------------------------------------
1 | # %%
2 |
3 | import pandas as pd
4 |
5 | # %%
6 |
7 | pd.Series()
--------------------------------------------------------------------------------
/dia01/02_series.py:
--------------------------------------------------------------------------------
1 | # %%
2 |
3 | idades = [
4 | 32, 38, 30, 30, 31,
5 | 35, 25, 29, 31, 37,
6 | 27, 23, 36, 33, 32,
7 | ]
8 |
9 | media = sum(idades) / len(idades)
10 | print("Media:",media)
11 |
12 | diffs = 0
13 | for i in idades:
14 | diffs += (i - media) ** 2
15 |
16 | variancia = diffs / (len(idades)-1)
17 |
18 | print("Variância:", variancia)
19 |
20 | # %%
21 |
22 | import pandas as pd
23 |
24 | idades = [
25 | 32, 38, 30, 30, 31,
26 | 35, 25, 29, 31, 37,
27 | 27, 23, 36, 33, 32,
28 | ]
29 |
30 | series_idades = pd.Series(idades)
31 | series_idades
32 |
33 | # %%
34 | # Estatísticas da séries
35 | media_idades = series_idades.mean()
36 | var_idades = series_idades.var()
37 | summary_idades = series_idades.describe()
38 | summary_idades
--------------------------------------------------------------------------------
/dia01/03_series_index.py:
--------------------------------------------------------------------------------
1 | # %%
2 | import pandas as pd
3 |
4 | idades = [
5 | 32, 38, 30, 30, 31,
6 | 35, 25, 29, 31, 37,
7 | 27, 23, 36, 33, 39,
8 | ]
9 |
10 | series_idades = pd.Series(idades)
11 | series_idades
12 |
13 | # %%
14 | idades[0]
15 | series_idades[0]
16 |
17 | # %%
18 | series_idades[-1]
19 |
20 | # %%
21 | series_idades = series_idades.sort_values()
22 | series_idades
23 |
24 | # %%
25 | series_idades[0]
26 |
27 | # %%
28 |
29 | series_idades.iloc[0]
30 |
31 | # %%
32 | series_idades.iloc[-1]
33 |
34 | # %%
35 |
36 | series_idades.iloc[:3]
37 | # %%
38 |
39 | series_idades.iloc[::-1]
40 |
41 | # %%
42 |
43 | idades = [
44 | 32, 38, 30, 30, 31,
45 | 35, 25, 29, 31, 37,
46 | 27, 23, 36, 33, 39,
47 | ]
48 |
49 | indexs = [
50 | "Téo", "Maria", "Jose", "Luis", "Ana",
51 | "Nah", "Dani", "Mah", "Fer", "Nanda",
52 | "Naty", "Nih", "Pedro", "Kozato", "Kozato",
53 | ]
54 |
55 | series_idades = pd.Series(idades, index=indexs)
56 | series_idades
57 |
58 | # %%
59 | series_idades.iloc[-1]
--------------------------------------------------------------------------------
/dia01/04_dataframes.py:
--------------------------------------------------------------------------------
1 | # %%
2 |
3 | import pandas as pd
4 |
5 | idades = [
6 | 32, 38, 30, 30, 31,
7 | 35, 25, 29, 31, 37,
8 | 27, 23, 36, 33, 39,
9 | ]
10 |
11 | nomes = [
12 | "Téo", "Maria", "Jose", "Luis", "Ana",
13 | "Nah", "Dani", "Mah", "Fer", "Nanda",
14 | "Naty", "Nih", "Pedro", "Kozato", "Kozato",
15 | ]
16 |
17 | series_idades = pd.Series(idades)
18 | series_nomes = pd.Series(nomes)
19 |
20 | # %%
21 |
22 | df = pd.DataFrame()
23 | df["idades"] = series_idades
24 | df["nomes"] = series_nomes
25 | df
26 |
27 | # %%
28 |
29 | df.iloc[0]["nomes"]
30 |
31 | # %%
32 |
33 | df.iloc[-1]["idades"]
--------------------------------------------------------------------------------
/dia02/01_csv.py:
--------------------------------------------------------------------------------
1 | # %%
2 |
3 | import pandas as pd
4 |
5 | df = pd.read_csv("../data/clientes.csv")
6 | df
7 |
8 | # %%
9 |
10 | df.to_csv("clientes.csv", index=False)
11 |
12 | # %%
13 |
14 | df.to_parquet("clientes.parquet", index=False)
15 |
16 | # %%
17 |
18 | df_2 = pd.read_parquet("clientes.parquet")
19 | df_2
20 |
21 | # %%
22 |
23 | df.to_excel("clientes.xlsx", index=False)
24 |
25 | # %%
26 |
27 | df_3 = pd.read_excel("clientes.xlsx")
28 | df_3
29 |
30 | # %%
31 |
32 | df_bobo = pd.read_csv("../data/bobo.csv", sep=";")
33 | df_bobo
34 |
--------------------------------------------------------------------------------
/dia02/02_clipboard.py:
--------------------------------------------------------------------------------
1 | # %%
2 |
3 | import pandas as pd
4 |
5 | df = pd.read_clipboard(sep=";")
6 | df
--------------------------------------------------------------------------------
/dia02/03_html.py:
--------------------------------------------------------------------------------
1 | # %%
2 |
3 | import pandas as pd
4 |
5 | url = "https://pt.wikipedia.org/wiki/Unidades_federativas_do_Brasil"
6 | dfs = pd.read_html(url)
7 | dfs
8 |
9 | # %%
10 |
11 | df_uf = dfs[1]
12 | df_uf.to_csv("ufs.csv", sep=";", index=False)
--------------------------------------------------------------------------------
/dia02/04_dataframes.py:
--------------------------------------------------------------------------------
1 | # %%
2 |
3 | import pandas as pd
4 |
5 | df_clientes = pd.read_csv("../data/clientes.csv")
6 | df_clientes
7 |
8 |
9 | # %%
10 | ## AMOSTRAS
11 |
12 | df_clientes.head(n=10)
13 |
14 | # %%
15 | df_clientes.tail(10)
16 |
17 | # %%
18 | df_clientes.sample(10)
19 |
20 | # %%
21 | df_clientes.shape
22 |
23 | # %%
24 | df_clientes.columns
25 |
26 | # %%
27 | df_clientes.index
28 |
29 | # %%
30 | df_clientes.info(memory_usage='deep', max_cols=2)
31 |
32 | # %%
33 | df_clientes.dtypes
--------------------------------------------------------------------------------
/dia02/05_colunas.py:
--------------------------------------------------------------------------------
1 | # %%
2 |
3 | import pandas as pd
4 |
5 | df = pd.read_csv("../data/transacoes.csv")
6 | df
7 |
8 | # %%
9 | df.shape
10 |
11 | # %%
12 | df.info(memory_usage="deep")
13 |
14 | # %%
15 | df.dtypes
16 |
17 | # %%
18 |
19 | renamed_columns = {
20 | "qtdePontos": "qtPontos",
21 | "descSistemaOrigem": "SistemaOrigem"
22 | }
23 |
24 | # df = df.rename(columns=renamed_columns)
25 | df.rename(columns=renamed_columns, inplace=True)
26 |
27 | # %%
28 |
29 | colunas = ["idCliente", "qtPontos"]
30 | df[colunas]
31 |
32 | # %%
33 | # SELECT * FROM df
34 | df
35 |
36 | # %%
37 | # SELECT idCliente FROM df
38 |
39 | df[["idCliente"]]
40 |
41 | # %%
42 |
43 | # SELECT idCliente, qtPontos FROM df LIMIT 5
44 | df[["idCliente", "qtPontos"]].tail(5)
45 |
46 | # %%
47 |
48 | # SELECT idCliente, idTransacao, qtPontos
49 | # FROM df
50 | # LIMIT 5
51 |
52 | df[["idCliente", "idTransacao", "qtPontos"]].head(5)
53 |
54 | # %%
55 |
56 | colunas = list(df.columns)
57 | colunas.sort()
58 | colunas
59 |
60 | df = df[colunas]
61 | df
--------------------------------------------------------------------------------
/dia03/01_filtro.py:
--------------------------------------------------------------------------------
1 | # %%
2 |
3 | import pandas as pd
4 |
5 | # %%
6 |
7 | pontos = [10, 1, 1, 1, 50, 100, 130, 30, 25, 50]
8 | filtro = []
9 |
10 | valores_50_mais = []
11 | for i in pontos:
12 | filtro.append(i>=50)
13 |
14 |
15 | resultado = []
16 | for i in range(len(pontos)):
17 | if filtro[i]:
18 | resultado.append(pontos[i])
19 |
20 |
21 | resultado
22 | filtro
23 | # %%
24 |
25 | brinquedo = pd.DataFrame(
26 | {
27 | "nome": ["teo", "nah", "mah"],
28 | "idade": [32,35,14],
29 | "uf": ["sp", "pr", "rj"],
30 | }
31 | )
32 |
33 | filtro = brinquedo["idade"] >= 18
34 | brinquedo[filtro]
35 |
36 | # %%
37 |
38 | df = pd.read_csv("../data/transacoes.csv")
39 | df.head()
40 |
41 | # %%
42 |
43 | # valores maiores que 50
44 | filtro = df["qtdePontos"] >= 50
45 | df[filtro]
46 |
47 | # %%
48 | # valores entre 50 (inclusive) e 100
49 | filtro = (df["qtdePontos"] >= 50) & (df["qtdePontos"] < 100)
50 | filtro
51 | df[filtro]
52 |
53 | # %%
54 |
55 | filtro = (df["qtdePontos"] == 1) | (df["qtdePontos"] == 100)
56 | df[filtro]
57 |
58 | # %%
59 | # pontos entre 0 e 50 ou do ano de 2025 para frente
60 |
61 | filtro = (df["qtdePontos"] > 0) & (df["qtdePontos"]<=50) | (df["dtCriacao"]>='2025-01-01')
62 | df[filtro]
63 |
64 | # %%
65 |
66 | True and True = True
67 | True and False = False
68 | False and True = False
69 | False and False = False
70 |
71 | True or True = True
72 | True or False = True
73 | False or True = True
74 | False or False = False
--------------------------------------------------------------------------------
/dia03/02_mais_filtros.py:
--------------------------------------------------------------------------------
1 | # %%
2 | import pandas as pd
3 |
4 | df = pd.read_csv("../data/transacao_produto.csv")
5 | df
6 |
7 | # %%
8 |
9 | filtro = (df["idProduto"] == 5) | (df["idProduto"] == 11)
10 | df[filtro]
11 |
12 | # %%
13 |
14 | filtro = df["idProduto"].isin([5,11])
15 | df[filtro]
16 |
17 | # %%
18 |
19 | clientes = pd.read_csv("../data/clientes.csv")
20 | clientes.head()
21 |
22 | filtro = clientes["dtCriacao"].notna()
23 | clientes[filtro]
24 |
25 | # %%
26 |
27 | ~clientes["dtCriacao"].isna()
28 | clientes["dtCriacao"].notna()
--------------------------------------------------------------------------------
/dia03/03_filtros_view.py:
--------------------------------------------------------------------------------
1 | # %%
2 |
3 | import pandas as pd
4 |
5 | clientes = pd.read_csv("../data/clientes.csv")
6 | clientes.head()
7 |
8 | # %%
9 |
10 | filtro = clientes["qtdePontos"] == 0
11 | clientes_0 = clientes[filtro].copy()
12 |
13 | clientes_0["flag_1"] = 1
14 | clientes_0
15 |
16 | # %%
17 |
18 | A = [1,2]
19 | B = A.copy()
20 | print("A:", A)
21 | print("B:", B)
22 |
23 | B.append("fodase")
24 | print("A:", A)
25 | print("B:", B)
26 |
--------------------------------------------------------------------------------
/dia04/01_novas_colunas.py:
--------------------------------------------------------------------------------
1 | # %%
2 |
3 | import pandas as pd
4 | import numpy as np
5 |
6 | df = pd.read_csv("../data/clientes.csv")
7 | df.head()
8 |
9 | # %%
10 |
11 | df["pontos_100"] = df["qtdePontos"] + 100
12 | df.head()
13 |
14 | # %%
15 |
16 | nova_coluna = []
17 | for i in df["qtdePontos"]:
18 | nova_coluna.append( i+100)
19 |
20 | nova_coluna
21 |
22 | # %%
23 |
24 | df["emailTwitch"] = df["flEmail"] + df["flTwitch"]
25 | df.head()
26 |
27 | # %%
28 | df["flEmail"] * df["flTwitch"]
29 |
30 | # %%
31 |
32 | df["qtdeSocial"] = df["flEmail"] + df["flTwitch"] + df["flYouTube"] + df["flBlueSky"] + df["flInstagram"]
33 | df
34 |
35 | # %%
36 |
37 | df["todas_social"] = df["flEmail"] * df["flTwitch"] * df["flYouTube"] * df["flBlueSky"] * df["flInstagram"]
38 | df
39 |
40 | # %%
41 | df["qtdePontos"]
42 |
43 | # %%
44 |
45 | df["logPontos"] = np.log(df["qtdePontos"]+1)
46 | df["logPontos"].describe()
47 |
48 | # %%
49 |
50 | import matplotlib.pyplot as plt
51 |
52 | plt.grid(True)
53 | plt.hist(df["logPontos"])
54 | plt.show()
--------------------------------------------------------------------------------
/dia04/02_sort.py:
--------------------------------------------------------------------------------
1 | # %%
2 |
3 | import pandas as pd
4 |
5 | clientes = pd.read_csv("../data/clientes.csv")
6 |
7 | max_ponto = clientes["qtdePontos"].max()
8 | filtro = clientes["qtdePontos"] == max_ponto
9 | clientes[filtro]
10 |
11 | # %%
12 |
13 | top_5 = (clientes.sort_values(by="qtdePontos", ascending=False)
14 | .head(5)["idCliente"] )
15 |
16 | type(top_5)
17 |
18 | # %%
19 | clientes
20 |
21 | # %%
22 |
23 | brinquedo = pd.DataFrame(
24 | {
25 | "nome": ["teo", "ana", "nah", "jose"],
26 | "idade": [32, 43, 35, 42],
27 | "salario":[2345, 4533, 3245, 4533],
28 | }
29 | )
30 |
31 | brinquedo
32 |
33 | # %%
34 |
35 | brinquedo.sort_values(by=["salario", "idade"], ascending=[False, True])
--------------------------------------------------------------------------------
/dia05/01_conv_tipo.py:
--------------------------------------------------------------------------------
1 | # %%
2 |
3 | import pandas as pd
4 |
5 | # %%
6 |
7 | df = pd.read_csv("../data/clientes.csv")
8 |
9 | # %%
10 |
11 | df["qtdePontos"].astype(float).astype(str)
12 |
13 | # %%
14 |
15 | replace = {"0000-00-00 00:00:00.000": "2024-02-01 09:00:00.000"}
16 |
17 | df["dtCriacao"] = pd.to_datetime(df["dtCriacao"].replace(replace))
18 |
19 | # %%
20 |
21 | df["dtCriacao"].dt.month
--------------------------------------------------------------------------------
/dia05/02_na.py:
--------------------------------------------------------------------------------
1 | # %%
2 |
3 | import pandas as pd
4 |
5 | clientes = pd.read_csv("../data/clientes.csv")
6 | clientes
7 |
8 | # %%
9 |
10 | clientes.dropna(how="any")
11 |
12 | # %%
13 |
14 | df = pd.DataFrame(
15 | {
16 | "nome": ["Téo", None, "Nah", "Marcio"],
17 | "idade": [None, None, 43, 52],
18 | "salario": [3453,4324,None,5423]
19 | }
20 | )
21 |
22 | # df
23 | df.dropna(how="all", subset=["idade", "nome"])
24 |
25 | # %%
26 |
27 | df["idade"] = df["idade"].fillna(0)
28 | df
29 |
30 | # %%
31 |
32 | df.fillna({"nome": "alguem", "idade": 0})
33 |
34 | # %%
35 | medias = df[['idade', 'salario']].mean()
36 | df.fillna(medias)
37 |
38 | # %%
39 |
40 | df["idade"].fillna(df["idade"].mean()).mean()
--------------------------------------------------------------------------------
/dia05/03_duplicatas.py:
--------------------------------------------------------------------------------
1 | # %%
2 |
3 | import pandas as pd
4 |
5 | # %%
6 |
7 | df = pd.DataFrame({
8 | "nome": ['teo', 'lara', 'nah', 'bia', 'mah', 'lara', 'mah', 'mah'],
9 | "sobrenome": ['calvo', 'calvo', 'ataide', 'ataide', 'silva', 'silva', 'silva', 'silva'],
10 | "salario": [2132, 1231, 454, 6543, 6532, 4322, 987, 2134],
11 | })
12 |
13 | df
14 |
15 | # %%
16 |
17 | df = (df.sort_values("salario", ascending=False)
18 | .drop_duplicates(keep='last', subset=["nome", "sobrenome"]))
19 |
20 | df
--------------------------------------------------------------------------------
/dia06/01_apply.py:
--------------------------------------------------------------------------------
1 | # %%
2 |
3 | import pandas as pd
4 |
5 | df = pd.read_csv("../data/clientes.csv")
6 | df.head()
7 |
8 | # %%
9 |
10 | def get_last_id(idCliente):
11 | return idCliente.split("-")[-1]
12 |
13 | # %%
14 |
15 | get_last_id("0033b737-8235-4c0f-9801-dc4ca185af00")
16 |
17 | # %%
18 |
19 | id_novo = []
20 |
21 | for i in df["idCliente"]:
22 | novo = get_last_id(i)
23 | id_novo.append(novo)
24 |
25 | df["novo_id"] = id_novo
26 | df.head()
27 |
28 | # %%
29 |
30 | df["idCliente"].apply(get_last_id)
31 |
32 |
33 | # %%
34 |
--------------------------------------------------------------------------------
/dia06/02_ufs.py:
--------------------------------------------------------------------------------
1 | # %%
2 |
3 | import pandas as pd
4 |
5 | url = "https://pt.wikipedia.org/wiki/Unidades_federativas_do_Brasil"
6 |
7 | dfs = pd.read_html(url)
8 | uf = dfs[1]
9 | uf
10 | # %%
11 |
12 | def str_to_float(x:str):
13 | x = (x.replace(" ", "")
14 | .replace(",", ".")
15 | .replace("\xa0", "")
16 | )
17 | return float(x)
18 |
19 | # %%
20 |
21 | uf["Área (km²)"] = uf["Área (km²)"].apply(str_to_float)
22 | uf["População (Censo 2022)"] = uf["População (Censo 2022)"].apply(str_to_float)
23 | uf["PIB (2015)"] = uf["PIB (2015)"].apply(str_to_float)
24 | uf["PIB per capita (R$) (2015)"] = uf["PIB per capita (R$) (2015)"].apply(str_to_float)
25 |
26 | # %%
27 |
28 | def exp_to_anos(exp:str):
29 | return float(exp.replace(",", ".")
30 | .replace(" anos", ""))
31 |
32 | uf["Expectativa de vida (2016)"] = uf["Expectativa de vida (2016)"].apply(exp_to_anos)
33 |
34 | # %%
35 |
36 | def uf_to_regiao(uf):
37 |
38 | # tartar uf
39 | # uf = uf
40 |
41 | if uf in ["Distrito Federal", "Goiás", "Mato Grosso", "Mato Grosso do Sul"]:
42 | return "Centro-Oeste"
43 | elif uf in ["Alagoas","Bahia", "Ceará", "Maranhão", "Paraíba", "Pernambuco", "Piauí", "Rio Grande do Norte", "Sergipe"]:
44 | return "Nordeste"
45 | elif uf in ["Acre", "Amapá", "Amazonas", "Pará", "Rondônia", "Roraima", "Tocantins"]:
46 | return "Norte"
47 | elif uf in ["Espírito Santo","Minas Gerais", "Rio de Janeiro", "São Paulo"]:
48 | return "Sudeste"
49 | elif uf in ["Paraná", "Rio Grande do Sul", "Santa Catarina"]:
50 | return "Sul"
51 |
52 | uf["Região"] = uf["Unidade federativa"].apply(uf_to_regiao)
53 |
54 | # %%
55 |
56 | def mortalidade_to_float(x:str):
57 | x = float(x.replace("‰", "")
58 | .replace(",", ".")
59 | )
60 | return x
61 |
62 | uf["Mortalidade infantil (/1000)"] = uf["Mortalidade infantil (2016)"].apply(mortalidade_to_float)
63 |
64 | # %%
65 |
66 | # %%
67 |
68 | # Se PIB / Capita > 30.000
69 | # +
70 | # Mort Infantil < 15 / 1000
71 | # +
72 | # IDH (2010) > 700
73 | # -> "Parece bom"
74 |
75 | # Nao parece bom
76 |
77 | # %%
78 |
79 | def classifica_bom(linha):
80 | return (linha["PIB per capita (R$) (2015)"] > 30000 and
81 | linha["Mortalidade infantil (/1000)"] < 15 and
82 | linha["IDH (2010)"] > 700)
83 |
84 | # %%
85 |
86 | uf.apply(classifica_bom, axis=1)
87 |
88 | # %%
89 |
90 | uf.apply(lambda x: x["PIB per capita (R$) (2015)"], axis=1)
--------------------------------------------------------------------------------
/dia07/01_summary.py:
--------------------------------------------------------------------------------
1 | # %%
2 | import pandas as pd
3 |
4 | idades = [32,44,12,54,67,32,23,34,32,12,45,43,28,73,29]
5 | idades = pd.Series(idades)
6 | idades.sum()
7 | idades.min()
8 | idades.max()
9 | idades.mean()
10 | idades.describe()
11 |
12 | # %%
13 | clientes = pd.read_csv("../data/clientes.csv")
14 | clientes
15 |
16 | # %%
17 | clientes["flTwitch"].sum()
18 | clientes["flTwitch"].mean()
19 |
20 | # %%
21 | redes_sociais = ["flEmail","flTwitch","flYouTube","flBlueSky","flInstagram"]
22 | clientes[redes_sociais].sum()
23 |
24 | # %%
25 | num_columns = clientes.dtypes[~(clientes.dtypes == "object")].index.tolist()
26 | clientes[num_columns].mean()
27 |
28 | # %%
29 | clientes[num_columns].describe()
30 |
31 | # %%
32 | clientes[["flTwitch", "flEmail"]].describe()
--------------------------------------------------------------------------------
/dia07/02_group.py:
--------------------------------------------------------------------------------
1 | # %%
2 |
3 | import pandas as pd
4 |
5 | transacoes = pd.read_csv("../data/transacoes.csv")
6 | transacoes.head()
7 |
8 | # %%
9 | transacoes.groupby(by=["idCliente"]).count()
10 |
11 | # %%
12 | transacoes.groupby(by=["idCliente"], as_index=False)[["idTransacao"]].count()
13 |
14 | # %%
15 | # qtde_transacoes
16 | # total_pontos
17 | # pontos / transacoes
18 |
19 | summary = (transacoes.groupby(by=["idCliente"], as_index=False)
20 | .agg({
21 | "idTransacao": ['count'],
22 | "qtdePontos": ['sum', 'mean']
23 | }))
24 |
25 | summary
26 |
27 | # %%
28 | summary[('qtdePontos','mean')]
29 |
30 | # %%
31 | summary.columns = ['idCliente', 'qtdeTransacao', "totalPontos", "avgPontos"]
32 | summary
--------------------------------------------------------------------------------
/dia07/03_custom_group.py:
--------------------------------------------------------------------------------
1 | # %%
2 |
3 | import pandas as pd
4 | import numpy as np
5 |
6 | transacoes = pd.read_csv("../data/transacoes.csv")
7 | transacoes.head()
8 |
9 | # %%
10 |
11 | def diff_amp(x: pd.Series):
12 | amplitude = x.max() - x.min()
13 | media = x.mean()
14 | return np.sqrt((amplitude-media)**2)
15 |
16 |
17 | def life_time(x: pd.Series):
18 | dt = pd.to_datetime(x)
19 | return (dt.max() - dt.min()).days
20 |
21 | idades = pd.Series([21,32,43,32,14,65,78,34,19])
22 |
23 | # %%
24 |
25 | summary = (transacoes.groupby(by=["idCliente"], as_index=False)
26 | .agg({
27 | "idTransacao": ['count'],
28 | "qtdePontos": ["sum", "mean", diff_amp],
29 | "dtCriacao": [life_time]
30 | })
31 | )
32 |
33 | summary.columns = [
34 | "idCliente",
35 | "qtdeTransacao",
36 | "totalPontos",
37 | "mediaPontos",
38 | "ampMeanDiff",
39 | "lifeTime",
40 | ]
41 |
42 | summary
43 | # %%
44 |
--------------------------------------------------------------------------------
/dia08/01_merge.py:
--------------------------------------------------------------------------------
1 | # %%
2 |
3 | import pandas as pd
4 |
5 | transacoes = pd.read_csv("../data/transacoes.csv")
6 | transacoes.head()
7 |
8 | # %%
9 | clientes = pd.read_csv("../data/clientes.csv")
10 | clientes.head()
11 |
12 | # %%
13 |
14 | transacoes.merge(
15 | right=clientes,
16 | how='left',
17 | on=['idCliente'],
18 | suffixes=["Transacao", "Cliente"],
19 | )
20 |
21 | # %%
22 |
23 | df_1 = pd.DataFrame({
24 | "transacao": [1,2,3,4,5],
25 | "nome": ["t1", "t2", "t3", "t4", "t5"],
26 | "idCliente": [1,2,3,2,2],
27 | "valor": [10,45,32,17,87],
28 | })
29 |
30 | df_2 = pd.DataFrame({
31 | "id": [1,2,3,4],
32 | "nome": ["teo", "nah", "mah", "jose"],
33 | })
34 |
35 | df_1.merge(
36 | df_2,
37 | left_on=["idCliente"],
38 | right_on=["id"],
39 | how='left',
40 | suffixes=["Transacao", "Cliente"],
41 | )
42 |
43 | # %%
44 |
--------------------------------------------------------------------------------
/dia08/02_concat.py:
--------------------------------------------------------------------------------
1 | # %%
2 |
3 | import pandas as pd
4 |
5 | df = pd.DataFrame({
6 | "cliente": [1,2,3,4,5],
7 | "nome": ["teo", "jose", "nah", "mah", "lah"],
8 | })
9 |
10 | df_02 = pd.DataFrame({
11 | "cliente": [6,7,8],
12 | "nome": ["kozato", "laura", "dan",],
13 | "idade":[32,29,31],
14 | })
15 |
16 | df_03 = pd.DataFrame({
17 | "idade": [32,34,19,54,33]
18 | })
19 |
20 | # %%
21 |
22 | dfs = [df, df_02]
23 |
24 | pd.concat(dfs, ignore_index=True)
25 |
26 | # %%
27 |
28 | df_03 = df_03.sort_values(by='idade').reset_index(drop=True)
29 | df_03
30 |
31 | # %%
32 |
33 | pd.concat([df, df_03], axis=1)
--------------------------------------------------------------------------------
/dia09/01_db.py:
--------------------------------------------------------------------------------
1 | # %%
2 | import pandas as pd
3 | import sqlalchemy
4 |
5 | # %%
6 |
7 | engine = sqlalchemy.create_engine("sqlite:///../data/olist.db")
8 |
9 | # %%
10 | clientes = pd.read_sql_table(table_name="tb_customers",
11 | con=engine)
12 |
13 | # %%
14 | clientes.shape
15 |
16 | # %%
17 |
18 | query = "SELECT * FROM tb_customers LIMIT 100"
19 |
20 | df_100 = pd.read_sql_query(query, con=engine)
21 | df_100
22 |
23 | # %%
24 |
25 |
--------------------------------------------------------------------------------
/dia09/02_etl.py:
--------------------------------------------------------------------------------
1 | # %%
2 | import pandas as pd
3 | import sqlalchemy
4 | from sklearn import cluster
5 |
6 | # %%
7 | with open("etl.sql") as open_file:
8 | query = open_file.read()
9 |
10 | print(query)
11 |
12 | # %%
13 | engine = sqlalchemy.create_engine("sqlite:///../data/olist.db")
14 | df = pd.read_sql_query(query, con=engine)
15 | df
16 |
17 | # %%
18 | ## Treina modelo de cluster
19 | kmean = cluster.KMeans(n_clusters=4)
20 | kmean.fit(df[['totalRevenue','qtSalles']])
21 |
22 | df["cluster"] = kmean.labels_
23 | df
24 |
25 | # %%
26 | df.to_sql(
27 | "sellers_cluster",
28 | con=engine,
29 | index=False,
30 | if_exists="replace",
31 | )
32 |
--------------------------------------------------------------------------------
/dia09/etl.sql:
--------------------------------------------------------------------------------
1 | SELECT seller_id,
2 | sum(t1.price) As totalRevenue,
3 | count(distinct t1.order_id) AS qtSalles
4 |
5 | FROM tb_order_items AS t1
6 |
7 | GROUP BY seller_id
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/dia09/pandas_sql.drawio:
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/dia09/pandas_sql.png:
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https://raw.githubusercontent.com/TeoMeWhy/pandas-2025/1f56a6f461ca014c36acdab69832e19e25216f30/dia09/pandas_sql.png
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/exercicios/02/01.py:
--------------------------------------------------------------------------------
1 | # Leia o arquivo transacoes.csv com a formatação correta;
2 | # Adicione uma coluna com valores 1;
3 | # Salve o dataframe com nome: transacoes_1.csv
4 |
5 | # %%
6 |
7 | import pandas as pd
8 |
9 | df = pd.read_csv("../../data/transacoes.csv")
10 |
11 | df["valor 1"] = 1
12 | df.head(5)
13 |
14 | df.to_csv("transacoes_1.csv", index=False)
--------------------------------------------------------------------------------
/exercicios/03/exercicios.py:
--------------------------------------------------------------------------------
1 | # %%
2 | # 03.01 - Quantas linhas há no arquivo clientes.csv ?
3 | import pandas as pd
4 |
5 | df_clientes = pd.read_csv("../../data/clientes.csv")
6 | linhas = df_clientes.shape[0]
7 |
8 | print(f"O arquivo de clientes.csv tem {linhas} linhas")
9 |
10 |
11 | # %%
12 | # 03.02 - Quantas colunas do tipo int há no arquivo transacoes.csv ?
13 |
14 | df_transacoes = pd.read_csv("../../data/transacoes.csv")
15 | df_transacoes.dtypes
16 |
17 | print("O arquivo transacoes.csv tem uma coluna do tipo int (qtdePontos)")
18 |
19 |
20 | # %%
21 | # 03.03 - Quantas colunas do tipo object há no arquivo produtos.csv ?
22 | df_produtos = pd.read_csv("../../data/produtos.csv")
23 | df_produtos.dtypes
24 |
25 | print("O arquivo produtos.csv tem 1 coluna do tipo object (descProduto)")
26 |
27 | # %%
28 | # 03.04 - Qual o id do cliente no índice 4 no arquivo clientes.csv ?
29 |
30 | # df_clientes['idCliente'][4]
31 | df_clientes.loc[4]["idCliente"]
32 |
33 | # %%
34 | # 03.05 - Qual o saldo de pontos do cliente na 10a posição (sem ordenar) do arquivo clientes.csv ?
35 |
36 | df_clientes.iloc[9]["qtdePontos"]
--------------------------------------------------------------------------------
/exercicios/04/drop_duplicates.py:
--------------------------------------------------------------------------------
1 | # %%
2 |
3 | # Obtenha a última linha de transacao de cada cliente
4 | # Obtenha a primeira
5 |
6 | import pandas as pd
7 |
8 | df = pd.read_csv("../../data/transacoes.csv")
9 | df.head()
10 |
11 | # %%
12 |
13 | # ultima
14 | ultima_transacao = (df.sort_values(by="dtCriacao")
15 | .drop_duplicates(subset=['idCliente'], keep='last'))
16 |
17 | # %%
18 | primeira_transacao = (df.sort_values(by="dtCriacao")
19 | .drop_duplicates(subset=['idCliente'], keep='first'))
--------------------------------------------------------------------------------
/exercicios/04/exercicios.py:
--------------------------------------------------------------------------------
1 | # %%
2 | import pandas as pd
3 |
4 | # %%
5 | # 04.01 - Quantos clientes tem vínculo com a Twitch?
6 |
7 | clientes = pd.read_csv("../../data/clientes.csv")
8 | clientes.head()
9 |
10 | filtro = clientes["flTwitch"] == 1
11 | qtde_twitch = clientes[filtro].shape[0]
12 |
13 | print(f"Temos {qtde_twitch} usuários com twitch")
14 |
15 | # %%
16 | # 04.02 - Quantos clientes tem um saldo de pontos maior que 1000?
17 |
18 | clientes.head()
19 |
20 | filtro = clientes["qtdePontos"] > 1000
21 | qtde_1000 = clientes[filtro].shape[0]
22 | print(f"Temos {qtde_1000} clientes com mais de 1000 pontos")
23 |
24 | # %%
25 | # 04.03 - Quantas transações ocorreram no dia 2025-02-01?
26 |
27 | transacoes = pd.read_csv("../../data/transacoes.csv")
28 | transacoes.head()
29 | filtro = (transacoes['dtCriacao'] >= '2025-02-01') & (transacoes['dtCriacao'] < '2025-02-02')
30 | qtde_dia_2025_02_01 = transacoes[filtro].shape[0]
31 | print(f"No dia 2025-02-01 tivemos {qtde_dia_2025_02_01} transacoes")
32 |
--------------------------------------------------------------------------------
/exercicios/05/exercicios.py:
--------------------------------------------------------------------------------
1 | # %%
2 |
3 | import pandas as pd
4 | import numpy as np
5 |
6 | # %%
7 | # 05.01 - Crie uma coluna nova “twitch_points” que á
8 | # resultado da multiplicação do saldo de pontos e a marcação da twitch
9 |
10 | clientes = pd.read_csv("../../data/clientes.csv")
11 |
12 | clientes["twitch_points"] = clientes["qtdePontos"] * clientes["flTwitch"]
13 | clientes.head()
14 |
15 | # %%
16 | # 05.02 - Aplique o log na coluna de saldo de pontos, criando uma coluna nova
17 |
18 | clientes["logPontos"] = np.log(clientes["qtdePontos"])
19 | clientes.head()
20 |
21 | # %%
22 | # 05.03 - Crie uma coluna que sinalize se a
23 | # pessoa tem vínculo com alguma (qualquer uma)
24 | # plataforma de rede social.
25 | clientes["ao_menos_uma_social"] = clientes["flTwitch"] + clientes["flYouTube"] + clientes["flBlueSky"] + clientes["flInstagram"]
26 | clientes.head()
27 |
28 | # 05.04 - Qual é o id de cliente que tem maior saldo de pontos?
29 | # E o menor?
30 |
31 | clientes.sort_values(by="qtdePontos", ascending=False).head(1)["idCliente"]
32 |
33 | clientes.sort_values(by="qtdePontos", ascending=True).head(1)["idCliente"]
34 |
35 | # %%
36 | # 05.05 - Selecione a primeira transação diária de cada cliente.
37 |
38 | import pandas as pd
39 |
40 | transacoes = pd.read_csv("../../data/transacoes.csv")
41 | transacoes.head()
42 |
43 | transacoes["data"] = pd.to_datetime(transacoes["dtCriacao"]).dt.date
44 | transacoes = transacoes.sort_values("dtCriacao")
45 |
46 | # %%
47 |
48 | first = transacoes.drop_duplicates(keep="first", subset=["idCliente", "data"])
49 | last = transacoes.drop_duplicates(keep="last", subset=["idCliente", "data"])
50 |
51 | pd.concat([last, first])
--------------------------------------------------------------------------------
/exercicios/06/01.py:
--------------------------------------------------------------------------------
1 | # %%
2 | # 06.01 - Qual a quantidade média de redes sociais dos usuários?
3 | # E a Variância? E o máximo?
4 |
5 | import pandas as pd
6 |
7 | clientes = pd.read_csv("../../data/clientes.csv")
8 | clientes
9 |
10 | # %%
11 |
12 | clientes["totalRedes"] = (clientes['flEmail'] +
13 | clientes['flTwitch'] +
14 | clientes['flYouTube'] +
15 | clientes['flBlueSky'] +
16 | clientes['flInstagram'])
17 |
18 | media = clientes["totalRedes"].mean()
19 | variancia = clientes["totalRedes"].var()
20 | maximo = clientes["totalRedes"].max()
21 |
22 | print("media:",media)
23 | print("variancia:",variancia)
24 | print("maximo:",maximo)
25 |
26 | # %%
27 |
28 | redes = [
29 | "flEmail",
30 | "flTwitch",
31 | "flYouTube",
32 | "flBlueSky",
33 | "flInstagram",
34 | ]
35 |
36 | clientes[redes].sum(axis=1).describe()
--------------------------------------------------------------------------------
/exercicios/06/02.py:
--------------------------------------------------------------------------------
1 | # %%
2 | # 06.02 - Quais são os usuários que mais fizeram transações?
3 | # Considere os 10 primeiros.
4 |
5 | import pandas as pd
6 |
7 | df = pd.read_csv("../../data/transacoes.csv")
8 |
9 | (df.groupby(by=['idCliente'])['idTransacao']
10 | .count()
11 | .sort_values(ascending=False)
12 | .head(10))
13 |
14 |
--------------------------------------------------------------------------------
/exercicios/06/03.py:
--------------------------------------------------------------------------------
1 | # %%
2 | # 06.03 - Qual usuário teve maior quantidade de pontos debitados?
3 |
4 | import pandas as pd
5 |
6 | transacoes = pd.read_csv("../../data/transacoes.csv")
7 | transacoes.head()
8 |
9 | # %%
10 |
11 | filtro = transacoes['qtdePontos'] < 0
12 |
13 | (transacoes[filtro].groupby(by='idCliente')['qtdePontos']
14 | .sum()
15 | .sort_values(ascending=True)
16 | .head(1))
--------------------------------------------------------------------------------
/exercicios/06/04.py:
--------------------------------------------------------------------------------
1 | # %%
2 | # 06.04 - Quem teve mais transações de Streak?
3 |
4 | import pandas as pd
5 |
6 | # %%
7 |
8 | transacoes = pd.read_csv("../../data/transacoes.csv")
9 | transacoes.head()
10 |
11 | # %%
12 | transacao_produto = pd.read_csv("../../data/transacao_produto.csv")
13 | transacao_produto.head()
14 |
15 | # %%
16 | produtos = pd.read_csv("../../data/produtos.csv")
17 | produtos.head()
18 |
19 | # %%
20 |
21 | cliente_transacao_produto = transacoes.merge(
22 | transacao_produto,
23 | on="idTransacao",
24 | how="left",
25 | )[['idTransacao', "idCliente", "idProduto"]]
26 |
27 | df_full = cliente_transacao_produto.merge(
28 | produtos,
29 | on=['idProduto'],
30 | how='left',
31 | )
32 |
33 | df_full = df_full[df_full["descProduto"]=="Presença Streak"]
34 |
35 | (df_full.groupby(by=["idCliente"])["idTransacao"]
36 | .count()
37 | .sort_values(ascending=False)
38 | .head(1)
39 | )
40 |
41 | # %%
42 |
43 | produtos = produtos[produtos["descProduto"]=="Presença Streak"]
44 |
45 | (transacoes.merge(transacao_produto, on=["idTransacao"], how="left")
46 | .merge(produtos, on=["idProduto"], how="inner")
47 | .groupby(by="idCliente")["idTransacao"]
48 | .count()
49 | .sort_values(ascending=False)
50 | .head(1)
51 | )
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/exercicios/06/05.py:
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1 | # %%
2 | # 06.05 - Qual a média de transações / dia?
3 |
4 | import pandas as pd
5 |
6 | df = pd.read_csv("../../data/transacoes.csv")
7 | df.head()
8 |
9 | # %%
10 | df["dtDia"] = pd.to_datetime(df["dtCriacao"]).dt.date
11 |
12 | summary = df.agg({
13 | "idTransacao": 'count',
14 | "dtDia": 'nunique',
15 | })
16 |
17 | transacoe_dia = summary["idTransacao"] / summary["dtDia"]
18 | transacoe_dia
19 |
20 | # %%
21 |
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/exercicios/06/06.py:
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1 | # %%
2 | # 06.06 - Como podemos calcular as estatísticas descritivas
3 | # dos pontos das transações de cada usuário?
4 |
5 | import pandas as pd
6 |
7 | transacoes = pd.read_csv("../../data/transacoes.csv")
8 | transacoes.head()
9 | # %%
10 |
11 | (transacoes.groupby(by=['idCliente'], as_index=False)['qtdePontos']
12 | .describe())
13 |
14 |
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/exercicios/case_cartao_credito/cartao.py:
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1 | # %%
2 |
3 | import pandas as pd
4 |
5 | df = pd.read_csv("dados_cartao.csv", sep=";")
6 | df
7 | # %%
8 |
9 | df["dtTransacao"] = pd.to_datetime(df["dtTransacao"])
10 | df
11 |
12 | # %%
13 |
14 | df["vlParcela"] = df["vlVenda"] / df["qtParcelas"]
15 | df
16 |
17 | # %%
18 |
19 | df["ordemParcela"] = df.apply(lambda row: [i for i in range(row['qtParcelas'])], axis=1)
20 | df
21 |
22 | # %%
23 |
24 | df_explode = df.explode("ordemParcela")
25 | df_explode
26 |
27 | # %%
28 |
29 | def calcDtParcela(row):
30 | dt = row["dtTransacao"] + pd.DateOffset(months=row["ordemParcela"])
31 | dt = f"{dt.year}-{dt.month}"
32 | return dt
33 |
34 | df_explode["dtParcela"] = df_explode.apply(calcDtParcela, axis=1)
35 | df_explode
36 | # %%
37 |
38 | (df_explode.groupby(["idCliente", "dtParcela"])
39 | ['vlParcela'].sum()
40 | .reset_index()
41 | .pivot_table(index='idCliente',
42 | columns='dtParcela',
43 | values='vlParcela',
44 | fill_value=0))
45 |
46 |
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/exercicios/case_homicidios/01_case.py:
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1 | # %%
2 | import pandas as pd
3 | import os
4 |
5 | def read_file(file_name:str):
6 | df = (pd.read_csv(f"../../data/ipea/{file_name}.csv", sep=";")
7 | .rename(columns={"valor":file_name})
8 | .set_index(["nome", "período"])
9 | .drop(["cod"],axis=1))
10 |
11 | return df
12 |
13 | # %%
14 | file_names = os.listdir("../../data/ipea/")
15 |
16 | dfs = []
17 | for i in file_names:
18 | file_name = i.split(".")[0]
19 | dfs.append(read_file(file_name))
20 |
21 |
22 | df_full = (pd.concat(dfs, axis=1)
23 | .reset_index()
24 | .sort_values(["período", "nome"]))
25 |
26 | df_full.to_csv("homicios_consolidado.csv", index=False, sep=";")
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/exercicios/case_homicidios/02_stack_unstack.py:
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1 | # %%
2 |
3 | import pandas as pd
4 |
5 | df = pd.read_csv("homicidios_consolidado.csv", sep=";")
6 | df.head()
7 |
8 | # %%
9 |
10 | df_stack = (df.set_index(["nome", "período"])
11 | .stack())
12 | df_stack = df_stack.reset_index()
13 | df_stack.columns = ["nome", "período", "metrica", "valor"]
14 | df_stack
15 |
16 | # %%
17 |
18 | df_unstack = (df_stack.set_index(["nome", "período", "metrica"])
19 | .unstack()
20 | .reset_index())
21 |
22 | df_unstack
23 | # %%
24 |
25 | df_unstack.columns.droplevel(0).tolist()[2:]
26 |
27 | # %%
28 |
29 | metricas = df_unstack.columns.droplevel(0)[2:].tolist()
30 | df_unstack.columns = ['nome', 'período'] + metricas
31 | df_unstack
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/exercicios/case_homicidios/03_pivot.py:
--------------------------------------------------------------------------------
1 | # %%
2 |
3 | import pandas as pd
4 |
5 | df = pd.read_csv("homicidios_consolidado.csv", sep=";")
6 | df.head()
7 |
8 | # %%
9 |
10 | df_stack = (df.set_index(["nome", "período"])
11 | .stack()
12 | .reset_index())
13 |
14 | df_stack.columns = ["nome", "periodo", "metrica", "valor"]
15 |
16 | df_stack
17 |
18 | # %%
19 |
20 | (df_stack.pivot_table(values="valor",
21 | index=["nome", "periodo"],
22 | columns="metrica")
23 | .reset_index())
24 |
25 | # %%
26 |
27 | df_stack.pivot_table(values="valor",
28 | index=["nome"],
29 | columns="metrica",
30 | aggfunc='min')
31 |
32 | # %%
33 |
34 | (df_stack.pivot_table(values="valor",
35 | index=["nome", "periodo"],
36 | columns="metrica")
37 | .stack()
38 | )
39 |
40 | # %%
41 |
42 | df_stack
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