Pandas 1. Hands-on python pandas intro.

To install pandas:

pip install pandas

[admin@localhost ~]$ cat >  test.csv

Rooms,Price,Floors,Area

1,300,1,30

1,400,1,50

3,400,1,65

2,200,1,45

5,700,3,120

,400,2,70

,300,1,40

4,,2,95

>>> import pandas as pd

>>> test_file_path = "~/test.csv"

>>> test_data = pd.read_csv(test_file_path)

>>> type(test_data)

<class 'pandas.core.frame.DataFrame'>

>>> help(pd.DataFrame)

class DataFrame(pandas.core.generic.NDFrame)

 |  Two-dimensional size-mutable, potentially heterogeneous tabular data

 |  structure with labeled axes (rows and columns). Arithmetic operations

 |  align on both row and column labels. Can be thought of as a dict-like

 |  container for Series objects. The primary pandas data structure.

>>> test_data

<output omitted - NaN means - missing value (Not a Number))>

>>> test_data.describe()

<output omitted - to read about mean, std, percentile go to it-tuff.blogspot.com/math-1 >

>>>  test_data.columns

Index([u'Rooms', u'Price', u'Floors', u'Area'], dtype='object')
>>> test_data.columns.values
array(['Rooms', 'Price', 'Floors', 'Area'], dtype=object)

>>> help(test_data.dropna)

dropna(self, axis=0, how='any', thresh=None, subset=None, inplace=False) method of pandas.core.frame.DataFrame instance

    Remove missing values.

    ----------

    axis : {0 or 'index', 1 or 'columns'}, default 0

        Determine if rows or columns which contain missing values are

        removed.

        * 0, or 'index' : Drop rows which contain missing values.

        * 1, or 'columns' : Drop columns which contain missing value.

>>> test_data = test_data.dropna(axis=0)

>>> test_data

<<output omitted - rows with NaN values are removed>

>>> test_data.describe()

<output omitted>

>>> test_data.columns.values
array(['Rooms', 'Price', 'Floors', 'Area'], dtype=object)

>>> price = test_data.Price

>>> type(price)

<class 'pandas.core.series.Series'>

>>> help(pd.Series)

class Series(pandas.core.base.IndexOpsMixin, pandas.core.generic.NDFrame)

 |  One-dimensional ndarray with axis labels (including time series).

>>> price

<output omitted>

>>> price.describe()

<output omitted - only copy of the Price column is shown>

>>> test_data.columns.values
array(['Rooms', 'Price', 'Floors', 'Area'], dtype=object)

>>> test_data_features=['Rooms','Price']

>>> features = test_data[test_data_features]

>>> type(features)

<class 'pandas.core.frame.DataFrame'>

>>> features

<output omitted - only copies of Rooms and Price columns are shown>

>>> features.describe()

<output omitted>

>>> features.head(n=3)

<output omitted - only first 3 rows are shown>

>>> help(features.head)

head(self, n=5) method of pandas.core.frame.DataFrame instance

    Return the first `n` rows.

>>> >>> test_data.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 5 entries, 0 to 4
Data columns (total 4 columns):
Rooms     5 non-null float64
Price     5 non-null float64
Floors    5 non-null int64
Area      5 non-null int64
dtypes: float64(2), int64(2)
memory usage: 200.0 bytes

>>>