¿Qué es pandas?
Introducción a la ciencia de datos con Python
Hillary Green-Lerman
Lead Data Scientist, Looker
¿Qué puede hacer pandas por ti?
- Cargar datos tabulares de varias fuentes
- Buscar filas o columnas concretas
- Calcular estadísticas agregadas
- Combinar datos de varias fuentes
Datos tabulares con pandas
Datos 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
Archivos CSV
Cargar un CSV
import pandas as pd
df = pd.read_csv('ransom.csv')
Mostrar un 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
Inspeccionar un 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
Inspeccionar un 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
Inspeccionar un DataFrame
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Introducción a la ciencia de datos con Python
