# Data processing

Most of the time of data analysis and modeling is spent on data preparation and processing i.e., loading, cleaning and rearranging the data, etc. Further, because of Python libraries, Pandas give us high performance, flexible, and high-level environment for processing the data. Various functionalities are available for pandas to process the data effectively.

Hierarchical indexing

For enhancing the capabilities of Data Processing, we have to use some indexing that helps to sort the data based on the labels. So, Hierarchical indexing is comes into the picture and defined as an essential feature of pandas that helps us to use the multiple index levels.

Creating multiple index

In Hierarchical indexing, we have to create multiple indexes for the data. This example creates a series with multiple indexes.

Example:

Output:

```aobj1   11
obj2   14
obj3   17
obj4   24
bobj1   19
obj2   32
obj3   34
obj4  27
dtype: int64
```

We have taken two level of index here i.e. (a, b) and (obj1,..., obj4) and can see the index by using 'index' command.

Output:

```MultiIndex(levels=[['x', 'y'], ['obj1', 'obj2', 'obj3', 'obj4']],
labels=[[0, 0, 0, 0, 1, 1, 1, 1], [0, 1, 2, 3, 0, 1, 2, 3]])
```

### Partial indexing

Partial indexing can be defined as a way to choose the particular index from a hierarchical indexing.

Below code is extracting 'b' from the data,

Output:

```obj1   19
obj2   32
obj3   34
obj4   27
dtype: int64
```

Further, the data can also be extracted based on inner level i.e. 'obj'. The below result defines two available values for 'obj2' in the Series.

Output:

```x   14
y 32
dtype: int64
```

### Unstack the data

Unstack means to change the row header to the column header. The row index will change to the column index, therefore the Series will become the DataFrame. Below are the example of unstacking the data.

Example:

Output:

```ab
obj1  11   19
obj2  14   32
obj3 17   34
obj4  24    27
# unstack based on second level i.e. 'obj'
info.unstack(1)
```

Output:

```obj1 obj2 obj3 obj4
a  11       14      17       24
b  19       32      34      27
```

'stack()' operation is used to convert the column index to row index. In above code, we can convert 'obj' as column index into row index using 'stack' operation.

Output:

```aobj1   11
obj2   14
obj3   17
obj4   24
bobj1   19
obj2   32
obj3   34
obj4  27
dtype: int64
```

### Column indexing

Remember that, since, column-indexing requires two dimensional data, the column indexing is possible only for DataFrame(not for Series). Let's create new DataFrame for demonstrating the columns with multiple index,

Output:

```num1 num2 num3
x           y             x
a one0 1 2
two3 4 5
b three 6 7 8
four 9 10 11
```

Output:

```MultiIndex(levels=[['x', 'y'], ['four', 'one', 'three', 'two']], labels=[[0, 0, 1, 1], [1, 3, 2, 0]])
```

Output:

```MultiIndex(levels=[['num1', 'num2', 'num3'], ['green', 'red']], labels=[[0, 1, 2], [1, 0, 1]])
```

### Swap and sort level

We can easily swap the index level by using 'swaplevel' command, which takes input as two level-numbers.

We can sort the labels by using 'sort_index' command. The data will be sorted by 'key2' names i.e. key2 that is arranged alphabetically.

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