In this tutorial, We create a series of 12 elements, random integers from 7000-10000, representing monthly income and set the index to be the month names, starting in January and ending in December.

## Create Random Integers

First, we create a series of 12 elements, with random integers from 7000-10000. To define a Pandas series, we call Series, passing it an iterable—typically, a Python list or NumPy array.

We can get a NumPy array of random integers by calling `np.random.randint()`

it’s three arguments to indicate the range (minimum and maximum) of the random numbers and how many we want.

`np.random.randint(7000, 10000, 12)`

First, the third argument indicates the length of the returned series. Second, the second argument is one more than the highest value we can get back. I can use them to create a series:

```
s = Series(np.random.randint(7000, 10000, 12))
print(s)
```

## Create Index

We now have a series of random integers between 7000 and 10000. But the index contains integers from 0 through 11—much as would be the case in a NumPy array.

There’s nothing inherently wrong with a numeric index, but Pandas gives us much more power and flexibility, letting us use a wide variety of data types, including strings.

We can change the index by assigning to the `index`

attribute:

```
s.index = 'Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec '.split()
print(s)
```

You can assign a list, NumPy array, or Pandas series as an index. However, the data structure you pass must be of the same length as the series. If it isn’t, you’ll get a ValueError exception, and the assignment will fail.

If we know what index we’ll want when we create the series, we can assign it to the index keyword parameter:

```
months = 'Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec '.split()
s = pd.Series(np.random.randint(7000, 10000, 12),index=months)
```

This is the preferred method for creating a series. That said, if and when I ever want to change the index, I can do that by assigning a new value to` s.index`

.

Pandas typically work with data in two-dimensional tables, known as “data frames,” with rows and columns. But each column in a data frame is built from a “series,” a one-dimensional data structure, which means that you can think of a data frame as a collection of series.

To convert Pandas Series to DataFrame you can pass Series as params within a `dict`

using the DataFrame constructor:

```
income_df=pd.DataFrame({'month':s.index, 'income':s.values})
income_df.head()
```

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