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# What kind of data does pandas handle?

- I want to start using pandas

      In [1]: import pandas as pd

  To load the pandas package and start working with it, import the package. The community agreed alias for pandas is `pd`, so loading pandas as `pd` is assumed standard practice for all of the pandas documentation.

## pandas data table representation

- I want to store passenger data of the Titanic. For a number of passengers, I know the name (characters), age (integers) and sex (male/female) data.

      In [2]: df = pd.DataFrame(
         ...:     {
         ...:         "Name": [
         ...:             "Braund, Mr. Owen Harris",
         ...:             "Allen, Mr. William Henry",
         ...:             "Bonnell, Miss Elizabeth",
         ...:         ],
         ...:         "Age": [22, 35, 58],
         ...:         "Sex": ["male", "male", "female"],
         ...:     }
         ...: )
         ...:

      In [3]: df
      Out[3]:
                             Name  Age     Sex
      0   Braund, Mr. Owen Harris   22    male
      1  Allen, Mr. William Henry   35    male
      2   Bonnell, Miss Elizabeth   58  female

  To manually store data in a table, create a `DataFrame`. When using a Python dictionary of lists, the dictionary keys will be used as column headers and the values in each list as columns of the `DataFrame`.

A `DataFrame` is a 2-dimensional data structure that can store data of different types (including characters, integers, floating point values, categorical data and more) in columns. It is similar to a spreadsheet, a SQL table or the `data.frame` in R.

- The table has 3 columns, each of them with a column label. The column labels are respectively `Name`, `Age` and `Sex`.

- The column `Name` consists of textual data with each value a string, the column `Age` are numbers and the column `Sex` is textual data.

- The index labels each row. By default, this is a sequence of integers starting at 0.

In spreadsheet software, the table representation of our data would look very similar:

## Each column in a `DataFrame` is a `Series`

- I’m just interested in working with the data in the column `Age`

      In [4]: df["Age"]
      Out[4]:
      0    22
      1    35
      2    58
      Name: Age, dtype: int64

  When selecting a single column of a pandas `DataFrame`, the result is a pandas `Series`. To select the column, use the column label in between square brackets `[]`.

Note

If you are familiar with Python dictionaries, the selection of a single column is very similar to the selection of dictionary values based on the key.

You can create a `Series` from scratch as well:

    In [5]: ages = pd.Series([22, 35, 58], name="Age")

    In [6]: ages
    Out[6]:
    0    22
    1    35
    2    58
    Name: Age, dtype: int64

A pandas `Series` has no column labels, as it is just a single column of a `DataFrame`. A Series does have row labels.

## Do something with a DataFrame or Series

- I want to know the maximum Age of the passengers

  We can do this on the `DataFrame` by selecting the `Age` column and applying `max()`:

      In [7]: df["Age"].max()
      Out[7]: 58

  Or to the `Series`:

      In [8]: ages.max()
      Out[8]: 58

As illustrated by the `max()` method, you can do things with a `DataFrame` or `Series`. pandas provides a lot of functionalities, each of them a method you can apply to a `DataFrame` or `Series`. As methods are functions, do not forget to use parentheses `()`.

- I’m interested in some basic statistics of the numerical data of my data table

      In [9]: df.describe()
      Out[9]:
                   Age
      count   3.000000
      mean   38.333333
      std    18.230012
      min    22.000000
      25%    28.500000
      50%    35.000000
      75%    46.500000
      max    58.000000

  The `describe()` method provides a quick overview of the numerical data in a `DataFrame`. As the `Name` and `Sex` columns are textual data, these are by default not taken into account by the `describe()` method.

Many pandas operations return a `DataFrame` or a `Series`. The `describe()` method is an example of a pandas operation returning a pandas `Series` or a pandas `DataFrame`.

To user guide

Check more options on `describe` in the user guide section about aggregations with describe

Note

This is just a starting point. Similar to spreadsheet software, pandas represents data as a table with columns and rows. Apart from the representation, the data manipulations and calculations you would do in spreadsheet software are also supported by pandas. Continue reading the next tutorials to get started!

#### REMEMBER

- Import the package, aka `import pandas as pd`

- A table of data is stored as a pandas `DataFrame`

- Each column in a `DataFrame` is a `Series`

- You can do things by applying a method on a `DataFrame` or `Series`

To user guide

A more extended explanation of `DataFrame` and `Series` is provided in the introduction to data structures page.