O que é pandas?
Introdução à Ciência de Dados em Python
Hillary Green-Lerman
Lead Data Scientist, Looker
O que o pandas faz por você?
- Carregar dados tabulares de fontes diversas
- Buscar linhas ou colunas específicas
- Calcular estatísticas agregadas
- Combinar dados de várias fontes
Dados tabulares com pandas
Dados tabulares
+-------------------------------------------------+
| suspect | location | price |
+-----------------------+-----------------+-------+
| Fred Frequentist | Petroleum Plaza | 24.95 |
| Ronald Aylmer Fisher | Clothing Club | 20.15 |
+-------------------------------------------------+
DataFrame
suspect location price
0 Fred Frequentist Perolium Plaza 24.95
1 Ronald Aylmer Fisher Clothing Club 20.15
Arquivos CSV
Carregando um CSV
import pandas as pd
df = pd.read_csv('ransom.csv')
Exibindo um DataFrame
df = pd.read_csv('filename.csv')print(df)
suspect location item price
0 Kirstine Smith Petroleum Plaza gas 24.95
1 Fred Frequentist Burger Mart fries 1.95
2 Gertrude Cox Burger Mart fries 1.95
3 Ronald Aylmer Fisher Clothing Club shirt 14.25
4 Kirstine Smith Clothing Club dress 20.15
5 Fred Frequentist Groceries R Us cucumbers 2.05
6 Kirstine Smith Clothing Club dress 20.15
7 Gertrude Cox Petroleum Plaza fizzy drink 1.90
8 Gertrude Cox Burger Mart fries 1.95
9 Ronald Aylmer Fisher Clothing Club shirt 14.25
10 Ronald Aylmer Fisher Petroleum Plaza carwash 13.25
11 Ronald Aylmer Fisher Clothing Club shirt 14.25
12 Kirstine Smith Petroleum Plaza gas 24.95
13 Fred Frequentist Groceries R Us eggs 6.50
14 Gertrude Cox Petroleum Plaza gas 24.95
15 Fred Frequentist Groceries R Us eggs 6.50
16 Ronald Aylmer Fisher Groceries R Us eggs 6.50
17 Fred Frequentist Groceries R Us cheese 5.00
Inspecionando um DataFrame
df.head()
print(df.head())
suspect location item price
0 Kirstine Smith Petroleum Plaza gas 24.95
1 Fred Frequentist Burger Mart fries 1.95
2 Gertrude Cox Burger Mart fries 1.95
3 Ronald Aylmer Fisher Clothing Club shirt 14.25
4 Kirstine Smith Clothing Club dress 20.15
Inspecionando um DataFrame
df.info()
print(df.info())
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 26 entries, 0 to 25
Data columns (total 3 columns):
# Column Non-Null Count Dtype
-- ------ -------------- -----
0 letter_index 26 non-null int64
1 letter 26 non-null object
2 frequency 26 non-null float64
dtypes: float64(1), int64(1), object(1)
memory usage: 704.0+ bytes
Inspecionando um DataFrame
Vamos praticar!
Introdução à Ciência de Dados em Python
