Intro to Pandas Series
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Files associated with this lesson:

Lecture.ipynb

rmotr

Intro to Pandas Series¶

A Series is a one-dimensional array-like object containing a typed sequence of values and an associated array of data labels, called its index.

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Hands on!¶

In [1]:

import numpy as np
import pandas as pd

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Series creation¶

pd.Series' constructor accepts the following parameters:

data: (required) has all the data we want to store on the Series and could be an scalar value, a Python sequence or an unidimensional NumPy ndarray.
index: (optional), has all the labels that we want to assign to our data values and could be a Python sequence or an unidimensional NumPy ndarray. Default value: np.arange(0, len(data)).
dtype: (optional) any NumPy data type.

In [2]:

series = pd.Series([1, 2, 3, 4, 5])
series

Out[2]:

0    1
1    2
2    3
3    4
4    5
dtype: int64

Series have an associated type:

In [3]:

# Show first values of our Series
series.head()

Out[3]:

0    1
1    2
2    3
3    4
4    5
dtype: int64

In [4]:

series.dtype

Out[4]:

dtype('int64')

In [5]:

series = pd.Series([1, 2, 3, 4, 5], dtype=np.float)
series

Out[5]:

0    1.0
1    2.0
2    3.0
3    4.0
4    5.0
dtype: float64

In [6]:

series.dtype

Out[6]:

dtype('float64')

In [7]:

series = pd.Series(['a', 'b', 'c', 'd', 'e'])
series

Out[7]:

0    a
1    b
2    c
3    d
4    e
dtype: object

In [8]:

# Using a ndarraynp.array([2, 4, 6, 8, 10
array = np.array([2, 4, 6, 8, 10])
series = pd.Series(array)
series

Out[8]:

0     2
1     4
2     6
3     8
4    10
dtype: int64

In [9]:

# With predefined index
series = pd.Series([1, 2, 3, 4, 5], index=['a', 'b', 'c', 'd', 'e'])
series

Out[9]:

a    1
b    2
c    3
d    4
e    5
dtype: int64

In [10]:

# Using a dictionary (index will be defined using keys)
series = pd.Series({'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5}, dtype=np.float64)
series

Out[10]:

a    1.0
b    2.0
c    3.0
d    4.0
e    5.0
dtype: float64

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Series attributes¶

These are the most common attributes to get information about a Series:

In [11]:

series = pd.Series(data=[1, 2, 3, 4, 5],
                   index=['a', 'b', 'c', 'd', 'e'],
                   dtype=np.float64)
series

Out[11]:

a    1.0
b    2.0
c    3.0
d    4.0
e    5.0
dtype: float64

In [12]:

# Type of our Series
series.dtype

Out[12]:

dtype('float64')

In [13]:

# Values of a series
series.values

Out[13]:

array([1., 2., 3., 4., 5.])

In [14]:

type(series.values)

Out[14]:

numpy.ndarray

In [15]:

# Index of a series
series.index

Out[15]:

Index(['a', 'b', 'c', 'd', 'e'], dtype='object')

In [16]:

# Dimension of the Series
series.ndim

Out[16]:

In [17]:

# Shape of the Series
series.shape

Out[17]:

(5,)

In [18]:

# Number of Series elements
series.size

Out[18]:

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The Group of Seven¶

We'll start analyzing "The Group of Seven". Which is a political formed by Canada, France, Germany, Italy, Japan, the United Kingdom and the United States. We'll start by analyzing population, and for that, we'll use a pandas.Series object.

In [19]:

# In millions
g7_pop = pd.Series([35.467, 63.951, 80.940, 60.665, 127.061, 64.511, 318.523])

g7_pop

Out[19]:

0     35.467
1     63.951
2     80.940
3     60.665
4    127.061
5     64.511
6    318.523
dtype: float64

Someone might not know we're representing population in millions of inhabitants. Series can have a name, to better document the purpose of the Series:

In [20]:

g7_pop.name = 'G7 Population in millions'

g7_pop

Out[20]:

0     35.467
1     63.951
2     80.940
3     60.665
4    127.061
5     64.511
6    318.523
Name: G7 Population in millions, dtype: float64

Series are pretty similar to numpy arrays:

In [21]:

g7_pop.dtype

Out[21]:

dtype('float64')

In [22]:

type(series.values)

Out[22]:

numpy.ndarray

In [23]:

g7_pop.ndim

Out[23]:

In [24]:

g7_pop.shape

Out[24]:

(7,)

In [25]:

g7_pop.size

Out[25]:

And they look like simple Python lists or Numpy Arrays. But they're actually more similar to Python dicts.

In [26]:

g7_pop

Out[26]:

0     35.467
1     63.951
2     80.940
3     60.665
4    127.061
5     64.511
6    318.523
Name: G7 Population in millions, dtype: float64

In [27]:

g7_pop.index

Out[27]:

RangeIndex(start=0, stop=7, step=1)

Assigning `Series` indexes¶

In contrast to lists, we can explicitly define the index:

In [28]:

g7_pop.index = [
    'Canada',
    'France',
    'Germany',
    'Italy',
    'Japan',
    'United Kingdom',
    'United States',
]

In [29]:

g7_pop

Out[29]:

Canada             35.467
France             63.951
Germany            80.940
Italy              60.665
Japan             127.061
United Kingdom     64.511
United States     318.523
Name: G7 Population in millions, dtype: float64

Compare it with the following table:

Removing indexes¶

We can also remove current indexes from our Series, going back to the original indexes. To do that we use the reset_index() method with drop=True parameter:

In [30]:

g7_pop

Out[30]:

Canada             35.467
France             63.951
Germany            80.940
Italy              60.665
Japan             127.061
United Kingdom     64.511
United States     318.523
Name: G7 Population in millions, dtype: float64

In [31]:

g7_pop.reset_index(drop=True)

Out[31]:

0     35.467
1     63.951
2     80.940
3     60.665
4    127.061
5     64.511
6    318.523
Name: G7 Population in millions, dtype: float64

In [32]:

g7_pop

Out[32]:

Canada             35.467
France             63.951
Germany            80.940
Italy              60.665
Japan             127.061
United Kingdom     64.511
United States     318.523
Name: G7 Population in millions, dtype: float64

Note that reset_index() will return a new Series, so if we want to keep it we need to assign it to a variable, or use inplace=True parameter to modify the original Series.

In [33]:

g7_pop.reset_index(drop=True, inplace=True)

In [34]:

g7_pop

Out[34]:

0     35.467
1     63.951
2     80.940
3     60.665
4    127.061
5     64.511
6    318.523
Name: G7 Population in millions, dtype: float64

Creating a `Series` with indexes already¶

We can create a new Series with its indexes labels in a single step:

In [35]:

values = [35.467, 63.951, 80.94, 60.665, 127.061, 64.511, 318.523]
indexes = ['Canada', 'France', 'Germany', 'Italy',
           'Japan', 'United Kingdom', 'United States']

pd.Series(values,
          index=indexes,
          name='G7 Population in millions')

Out[35]:

Canada             35.467
France             63.951
Germany            80.940
Italy              60.665
Japan             127.061
United Kingdom     64.511
United States     318.523
Name: G7 Population in millions, dtype: float64

Creating a `Series` from a data dictionary¶

We can say that Series look like "ordered dictionaries". We can actually create Series out of dictionaries:

In [36]:

data_dic = {
    'Canada': 35.467,
    'France': 63.951,
    'Germany': 80.94,
    'Italy': 60.665,
    'Japan': 127.061,
    'United Kingdom': 64.511,
    'United States': 318.523
}

g7_pop = pd.Series(data_dic,
                   name='G7 Population in millions')

In [37]:

g7_pop

Out[37]:

Canada             35.467
France             63.951
Germany            80.940
Italy              60.665
Japan             127.061
United Kingdom     64.511
United States     318.523
Name: G7 Population in millions, dtype: float64

Creating a `Series` out of other `Series`¶

You can also create Series out of other series, specifying indexes:

In [38]:

pd.Series(g7_pop,
          index=['France', 'Germany', 'Italy', 'Spain'])

Out[38]:

France     63.951
Germany    80.940
Italy      60.665
Spain         NaN
Name: G7 Population in millions, dtype: float64

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Intro to Pandas Series DONE check

Files associated with this lesson:

Intro to Pandas Series¶

Hands on!¶

Series creation¶

Series attributes¶

The Group of Seven¶

Assigning Series indexes¶

Removing indexes¶

Creating a Series with indexes already¶

Creating a Series from a data dictionary¶

Creating a Series out of other Series¶

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Intro to Pandas Series
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Assigning `Series` indexes¶

Creating a `Series` with indexes already¶

Creating a `Series` from a data dictionary¶

Creating a `Series` out of other `Series`¶