[pandas](http://pandas.pydata.org/) is a Python library for data analysis using data frames. Data frames are tables of data, which may conceptually be compared to a spreadsheet. Data scientists familiar with R will feel at home here. pandas is often used along with numpy, pyplot, and scikit-learn.
### Importing pandas
It is a widely used convention to import the pandas library using the alias `pd`:
A data frame consists of a number of rows and columns. Each column represents a feature of the data set, and so has a name and a data type. Each row represents a data point through associated feature values. The pandas library allows you to manipulate the data in a data frame in various ways. pandas has a lot of possibilities, so the following is merely scratching the surface to give a feel for the library.
Series is the basic data-type in pandas. A Series is very similar to an array (NumPy array) (in fact it is built on top of the NumPy array object). A Series can have axis labels, as it can be indexed by a label with no number indexing for the location of data. It can hold any valid Python Object like List, Dictionary, etc.
A `.csv` file is a *comma separated value* file. A very common way to store data. To load such data into a pandas data frame use the `read_csv` method:
Here, `file_path` can be a local path to a csv file on you computer, or an url pointing to one. The column names may be included in the csv file, or may be passed as an argument. For more on this, and much more, take a look at the [documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html?highlight=read_csv#pandas.read_csv).
A data frame can be subset in many ways. One of the simplest is getting a single column. For instance, if the data frame `df` contains a column named `age`, we can extract it as follows:
We can also extract a list of columns from the data frame. For instance, if the data frame contains columns `name`, `age` and `address`, we can get the `name` and `age` as follows:
Another option for subsetting a dataframe is using the loc and iloc methods. The difference between loc and iloc is that loc searches based on a label and iloc searches based on the integer value of a row or column. To perform the same indexing as the above example using loc: