Pandas Series

Question 1

data = pd.Series((0.25, 0.5, 0.75, 1.0), )

Answer 1

A Pandas Series is a one-dimensional array of indexed data. It can be created from a list or array as follows:

Question 2

data.values

Answer 2

The values are simply a familiar NumPy array

Question 3

data.index

Answer 3

The index is an array-like object of type pd.Index

Question 4

data[1:3]

Answer 4

Like with a NumPy array, data can be accessed by the associated index via the familiar Python square-bracket notation

Question 5

data = pd.Series([0.25, 0.5, 0.75, 1.0],
index=[‘a’, ‘b’, ‘c’, ‘d’])
data

Answer 5

This explicit index definition gives the Series object additional capabilities. For example, the index need not be an integer, but can consist of values of any desired type. For example, if we wish, we can use strings as an index:

Question 6

population_dict = {'California': 38332521,
                   'Texas': 26448193,
                   'New York': 19651127,
                   'Florida': 19552860,
                   'Illinois': 12882135}
population = pd.Series(population_dict)
population

Answer 6

The Series-as-dictionary analogy can be made even more clear by constructing a Series object directly from a Python dictionary

Question 7

population[‘California’:’Illinois’]

Answer 7

Unlike a dictionary, though, the Series also supports array-style operations such as slicing:

Question 8

pd.Series(5.0, index=[100, 200, 300])

Answer 8

data can be a scalar, which is repeated to fill the specified index:

Question 9

pd.Series({2:’a’, 1:’b’, 3:’c’}, index=[3])

Answer 9

Notice that in this case, the Series is populated only with the explicitly identified keys.

Question 10

states = pd.DataFrame((population, area), columns=[‘population’, ‘area’])

Answer 10

we can use a dictionary to construct a single two-dimensional object containing this information:
(Matching Indexes)

Question 11

states.index

Answer 11

Like the Series object, the DataFrame has an index attribute that gives access to the index labels:

Question 12

states.columns

Answer 12

Additionally, the DataFrame has a columns attribute, which is an Index object holding the column labels:

Question 13

A DataFrame is a collection of Series objects, and a single-column DataFrame can be constructed from a single Series:

Any list of dictionaries can be made into a DataFrame

Even if some keys in the dictionary are missing, Pandas will fill them in with NaN (i.e., “not a number”) values:

Answer 13

Dataframe

Question 14

pd.DataFrame({‘population’: population,

‘area’: area})

Answer 14

DataFrame can be constructed from a dictionary of Series objects as well:

Question 15

pd.DataFrame(np.random.rand(3, 2),
columns=[‘foo’, ‘bar’],
)

Answer 15

Given a two-dimensional array of data, we can create a DataFrame with any specified column and index names. If omitted, an integer index will be used for each:

Question 16

Index
ind[1] = 0
ERROR

Answer 16

One difference between Index objects and NumPy arrays is that indices are immutable–that is, they cannot be modified via the normal means:

Question 17

indA = pd.Index([1, 3, 5, 7, 9])
indB = pd.Index([2, 3, 5, 7, 11])

indA & indB # intersection
indA | indB # union
indA ^ indB # symmetric difference

Answer 17

Pandas objects are designed to facilitate operations such as joins across datasets, which depend on many aspects of set arithmetic. The Index object follows many of the conventions used by Python’s built-in set data structure, so that unions, intersections, differences, and other combinations can be computed in a familiar way: