TallerPythonIntroCienciaDatos

This is an excerpt from the Python Data Science Handbook by Jake VanderPlas; Jupyter notebooks are available on GitHub.

The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. If you find this content useful, please consider supporting the work by buying the book!

Introducción a los objetos de Pandas

< Manipulación de datos con Pandas | Contenido | Indices y selección de datos >

Open in Colab

In [1]:
import numpy as np
import pandas as pd

El objeto Series de Pandas

In [2]:
data = pd.Series([0.25, 0.5, 0.75, 1.0])
data
Out[2]:
0    0.25
1    0.50
2    0.75
3    1.00
dtype: float64
In [3]:
data.values
Out[3]:
array([ 0.25,  0.5 ,  0.75,  1.  ])
In [4]:
data.index
Out[4]:
RangeIndex(start=0, stop=4, step=1)
In [5]:
data[1]
Out[5]:
0.5
In [6]:
data[1:3]
Out[6]:
1    0.50
2    0.75
dtype: float64

Series es una generalización de NumPy array

In [7]:
data = pd.Series([0.25, 0.5, 0.75, 1.0],
                 index=['a', 'b', 'c', 'd'])
data
Out[7]:
a    0.25
b    0.50
c    0.75
d    1.00
dtype: float64
In [8]:
data['b']
Out[8]:
0.5
In [9]:
data = pd.Series([0.25, 0.5, 0.75, 1.0],
                 index=[2, 5, 3, 7])
data
Out[9]:
2    0.25
5    0.50
3    0.75
7    1.00
dtype: float64
In [10]:
data[5]
Out[10]:
0.5

Series como diccionarios especializados

In [11]:
population_dict = {'California': 38332521,
                   'Texas': 26448193,
                   'New York': 19651127,
                   'Florida': 19552860,
                   'Illinois': 12882135}
population = pd.Series(population_dict)
population
Out[11]:
California    38332521
Florida       19552860
Illinois      12882135
New York      19651127
Texas         26448193
dtype: int64
In [12]:
population['California']
Out[12]:
38332521
In [13]:
population['California':'Illinois']
Out[13]:
California    38332521
Florida       19552860
Illinois      12882135
dtype: int64

Construcción de un objeto Series

>>> pd.Series(data, index=index)
In [14]:
pd.Series([2, 4, 6])
Out[14]:
0    2
1    4
2    6
dtype: int64
In [15]:
pd.Series(5, index=[100, 200, 300])
Out[15]:
100    5
200    5
300    5
dtype: int64
In [16]:
pd.Series({2:'a', 1:'b', 3:'c'})
Out[16]:
1    b
2    a
3    c
dtype: object
In [17]:
pd.Series({2:'a', 1:'b', 3:'c'}, index=[3, 2])
Out[17]:
3    c
2    a
dtype: object

El objeto DataFrame de Pandas

DataFrame es una generalización de NumPy array

In [18]:
area_dict = {'California': 423967, 'Texas': 695662, 'New York': 141297,
             'Florida': 170312, 'Illinois': 149995}
area = pd.Series(area_dict)
area
Out[18]:
California    423967
Florida       170312
Illinois      149995
New York      141297
Texas         695662
dtype: int64
In [19]:
states = pd.DataFrame({'population': population,
                       'area': area})
states
Out[19]:
area population
California 423967 38332521
Florida 170312 19552860
Illinois 149995 12882135
New York 141297 19651127
Texas 695662 26448193
In [20]:
states.index
Out[20]:
Index(['California', 'Florida', 'Illinois', 'New York', 'Texas'], dtype='object')
In [21]:
states.columns
Out[21]:
Index(['area', 'population'], dtype='object')

DataFrame como un diccionario especializado

In [22]:
states['area']
Out[22]:
California    423967
Florida       170312
Illinois      149995
New York      141297
Texas         695662
Name: area, dtype: int64

Construcción de objetos DataFrame

In [23]:
pd.DataFrame(population, columns=['population'])
Out[23]:
population
California 38332521
Florida 19552860
Illinois 12882135
New York 19651127
Texas 26448193
In [24]:
data = [{'a': i, 'b': 2 * i}
        for i in range(3)]
pd.DataFrame(data)
Out[24]:
a b
0 0 0
1 1 2
2 2 4
In [25]:
pd.DataFrame([{'a': 1, 'b': 2}, {'b': 3, 'c': 4}])
Out[25]:
a b c
0 1.0 2 NaN
1 NaN 3 4.0
In [26]:
pd.DataFrame({'population': population,
              'area': area})
Out[26]:
area population
California 423967 38332521
Florida 170312 19552860
Illinois 149995 12882135
New York 141297 19651127
Texas 695662 26448193
In [27]:
pd.DataFrame(np.random.rand(3, 2),
             columns=['foo', 'bar'],
             index=['a', 'b', 'c'])
Out[27]:
foo bar
a 0.865257 0.213169
b 0.442759 0.108267
c 0.047110 0.905718
In [28]:
A = np.zeros(3, dtype=[('A', 'i8'), ('B', 'f8')])
A
Out[28]:
array([(0, 0.0), (0, 0.0), (0, 0.0)], 
      dtype=[('A', '<i8'), ('B', '<f8')])
In [29]:
pd.DataFrame(A)
Out[29]:
A B
0 0 0.0
1 0 0.0
2 0 0.0

El objeto Index de Pandas

In [30]:
ind = pd.Index([2, 3, 5, 7, 11])
ind
Out[30]:
Int64Index([2, 3, 5, 7, 11], dtype='int64')

Index en un array immutable

In [31]:
ind[1]
Out[31]:
3
In [32]:
ind[::2]
Out[32]:
Int64Index([2, 5, 11], dtype='int64')
In [33]:
print(ind.size, ind.shape, ind.ndim, ind.dtype)

5 (5,) 1 int64
In [34]:
ind[1] = 0

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-34-40e631c82e8a> in <module>()
----> 1 ind[1] = 0

/Users/jakevdp/anaconda/lib/python3.5/site-packages/pandas/indexes/base.py in __setitem__(self, key, value)
   1243 
   1244     def __setitem__(self, key, value):
-> 1245         raise TypeError("Index does not support mutable operations")
   1246 
   1247     def __getitem__(self, key):

TypeError: Index does not support mutable operations

Index como conjunto ordenado

In [35]:
indA = pd.Index([1, 3, 5, 7, 9])
indB = pd.Index([2, 3, 5, 7, 11])
In [36]:
indA & indB  # intersection
Out[36]:
Int64Index([3, 5, 7], dtype='int64')
In [37]:
indA | indB  # union
Out[37]:
Int64Index([1, 2, 3, 5, 7, 9, 11], dtype='int64')
In [38]:
indA ^ indB  # symmetric difference
Out[38]:
Int64Index([1, 2, 9, 11], dtype='int64')

< Manipulación de datos con Pandas | Contenido | Indices y selección de datos >

Open in Colab