Question

3. Define some functions of pandas with code: addo. sum().
subo​

Answer 1

Answer:

Explanation:

Essential basic functionality

Here we discuss a lot of the essential functionality common to the pandas data structures. To begin, let’s create some example objects like we did in the 10 minutes to pandas section:

In [1]: index = pd.date_range('1/1/2000', periods=8)

In [2]: s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])

In [3]: df = pd.DataFrame(np.random.randn(8, 3), index=index,

  ...:                   columns=['A', 'B', 'C'])

  ...:  

Head and tail

To view a small sample of a Series or DataFrame object, use the head() and tail() methods. The default number of elements to display is five, but you may pass a custom number.

In [4]: long_series = pd.Series(np.random.randn(1000))

In [5]: long_series.head()

Out[5]:  

0   -1.157892

1   -1.344312

2    0.844885

3    1.075770

4   -0.109050

dtype: float64

In [6]: long_series.tail(3)

Out[6]:  

997   -0.289388

998   -1.020544

999    0.589993

dtype: float64

Attributes and underlying data

pandas objects have a number of attributes enabling you to access the metadata

shape: gives the axis dimensions of the object, consistent with ndarray

Axis labels

Series: index (only axis)

DataFrame: index (rows) and columns

Note, these attributes can be safely assigned to!

In [7]: df[:2]

Out[7]:  

                  A         B         C

2000-01-01 -0.173215  0.119209 -1.044236

2000-01-02 -0.861849 -2.104569 -0.494929

In [8]: df.columns = [x.lower() for x in df.columns]

In [9]: df

Out[9]:  

                  a         b         c

2000-01-01 -0.173215  0.119209 -1.044236

2000-01-02 -0.861849 -2.104569 -0.494929

2000-01-03  1.071804  0.721555 -0.706771

2000-01-04 -1.039575  0.271860 -0.424972

2000-01-05  0.567020  0.276232 -1.087401

2000-01-06 -0.673690  0.113648 -1.478427

2000-01-07  0.524988  0.404705  0.577046

2000-01-08 -1.715002 -1.039268 -0.370647

Pandas objects (Index, Series, DataFrame) can be thought of as containers for arrays, which hold the actual data and do the actual computation. For many types, the underlying array is a numpy.ndarray. However, pandas and 3rd party libraries may extend NumPy’s type system to add support for custom arrays (see dtypes).

To get the actual data inside a Index or Series, use the .array property

In [10]: s.array

Out[10]:  

<PandasArray>

[ 0.4691122999071863, -0.2828633443286633, -1.5090585031735124,

-1.1356323710171934,  1.2121120250208506]

Length: 5, dtype: float64

In [11]: s.index.array

Out[11]:  

<PandasArray>

['a', 'b', 'c', 'd', 'e']

Length: 5, dtype: object