# Vectorized String Operations

## 1. INTRODUCING PANDAS STRING OPERATIONS

```python
import numpy as np
import pandas as pd 
```

> Vectorization is process of doing an operation on multiple items (in an array, for example) in one go.

```python
x  = np.array([1,2,3,4,5])

# performing vectorization of operations
x * 10
```

```
array([10, 20, 30, 40, 50])
```

However, it is not straightforward to perform vectorization on “array of strings” and Pandas addresses this need of performing vectorized string operations using various `str` methods

```python
names_series  = pd.Series(['tom','JOhn','MARIA'])
names_series
```

```
0      tom
1     JOhn
2    MARIA
dtype: object
```

```python
names_series.str.capitalize()
```

```
0      Tom
1     John
2    Maria
dtype: object
```

## 2. STRING OPERATIONS

Let’s first define a Pandas Series to work with:

```python
# Panda series use in this section
names = pd.Series(['Walter White', 'Jesse Pinkman', 'Skyler White', 'Hank Shrader', 'Mike Ehrmantraut', 'Gus Fring']) 
names
```

```
0        Walter White
1       Jesse Pinkman
2        Skyler White
3        Hank Shrader
4    Mike Ehrmantraut
5           Gus Fring
dtype: objec
```

### 2.1 Methods Similar to Python String Methods

Nearly all [Python’s built-in string methods](https://www.w3schools.com/python/python_ref_string.asp) are mirrored by a Pandas vectorized string method. Visit this [link](https://www.w3schools.com/python/python_ref_string.asp) to get the complete list.

```python
# lets apply some of these string methods to panda series
# to upper case
names.str.upper()
```

```
0        WALTER WHITE
1       JESSE PINKMAN
2        SKYLER WHITE
3        HANK SHRADER
4    MIKE EHRMANTRAUT
5           GUS FRING
dtype: objectt
```

```python
# to check if it is digit
names.str.isdigit()
```

```
0    False
1    False
2    False
3    False
4    False
5    False
dtype: bool
```

```python
# to get length of each item in the array
names.str.len()
```

```
0    12
1    13
2    12
3    12
4    16
5     9
dtype: int64
```

```python
# to get boolean array, one that passes the condition
names.str.startswith('W')
```

```
0     True
1    False
2    False
3    False
4    False
5    False
dtype: bool
```

### 2.2. String Methods using Regular Expressions

Regular expression is a special syntax to find string or set of strings. This topic is very broad and can be very dry. However, we are going to taste plain-vanilla flavor of them here.

The following methods accept regular expressions **to examine** ***the content of each string element***, and follow some of the API conventions of Python’s built-in `re` module

```python
# let apply str.extract() method with regular expression to extract the first names
names.str.extract('([A-Za-z]+)')
```

```
        0
0  Walter
1   Jesse
2  Skyler
3    Hank
4    Mike
5     Gus
```

There are some good introductory examples on regular expressions usage in Python [here](https://kanoki.org/2019/11/12/how-to-use-regex-in-pandas/)

### 2.3. Vectorized indexing and slicing

```python
# getting first letter of each element in the array
# using standard indexing method
names.str[0]
```

```
0    W
1    J
2    S
3    H
4    M
5    G
dtype: object
```

```python
# getting first letter of each element in the array
# using  str.get() method
names.str.get(0)
```

```
0    W
1    J
2    S
3    H
4    M
5    G
dtype: object
```

```python
# str.slice()
names.str.slice(0,2)
```

```
0    Wa
1    Je
2    Sk
3    Ha
4    Mi
5    Gu
dtype: object
```

```python
# str.split()
names.str.split()
```

```
0        [Walter, White]
1       [Jesse, Pinkman]
2        [Skyler, White]
3        [Hank, Shrader]
4    [Mike, Ehrmantraut]
5           [Gus, Fring]
dtype: object
```

```python
# str.split() with str.get(0) to get first name
names.str.split().str.get(0)
```

```
0    Walter
1     Jesse
2    Skyler
3      Hank
4      Mike
5       Gus
dtype: object
```

### 2.4. `get_dummies`

The `get_dummies()` lets you quickly split out indicator variables into a DataFrame

```python
dummy = pd.DataFrame({'info': ['A|B|C','A','A|C'],
                     'name': ['tom','dick','harry']})
print(dummy)
```

```
    info   name
0  A|B|C    tom
1      A   dick
2    A|C  harry
```

```python
# using get_dummies
print(dummy['info'].str.get_dummies('|'))
```

```
   A  B  C
0  1  1  1
1  1  0  0
2  1  0  1
```


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