# Pandas Apply Function

Consider the following data frame

<table><thead><tr><th width="106">total_bill</th><th>tip</th><th width="88">sex</th><th>smoker</th><th>day</th><th>time</th><th>size</th><th data-hidden></th></tr></thead><tbody><tr><td>16.99</td><td>1.01</td><td>Female</td><td>No</td><td>Sun</td><td>Dinner</td><td>2</td><td>0</td></tr><tr><td>10.34</td><td>1.66</td><td>Male</td><td>No</td><td>Sun</td><td>Dinner</td><td>3</td><td>1</td></tr><tr><td>21.01</td><td>3.50</td><td>Male</td><td>No</td><td>Sun</td><td>Dinner</td><td>3</td><td>2</td></tr><tr><td>23.68</td><td>3.31</td><td>Male</td><td>No</td><td>Sun</td><td>Dinner</td><td>2</td><td>3</td></tr><tr><td>24.59</td><td>3.61</td><td>Female</td><td>No</td><td>Sun</td><td>Dinner</td><td>4</td><td>4</td></tr></tbody></table>

When we run the following code, all values of **each column** will be printed

```python
df.apply(lambda x:print(x), axis=0)
```

```
    0      16.99
    1      10.34
    2      21.01
    3      23.68
    4      24.59
          ...
    Name: total_bill, Length: 244, dtype: float64
    0      1.01
    1      1.66
    2      3.50
    3      3.31
    4      3.61
          ...
    Name: tip, Length: 244, dtype: float64
    0      Female
    1        Male
    2        Male
    3        Male
    4      Female
            ...
    Name: sex, Length: 244, dtype: category
    Categories (2, object): ['Male', 'Female']
    0       No
    1       No
    2       No
    3       No
    4       No
          ...
    Name: smoker, Length: 244, dtype: category
    Categories (2, object): ['Yes', 'No']
    0       Sun
    1       Sun
    2       Sun
    3       Sun
    4       Sun
          ...
    Name: day, Length: 244, dtype: category
    Categories (4, object): ['Thur', 'Fri', 'Sat', 'Sun']
    0      Dinner
    1      Dinner
    2      Dinner
    3      Dinner
    4      Dinner
            ...
    Name: time, Length: 244, dtype: category
    Categories (2, object): ['Lunch', 'Dinner']
    0      2
    1      3
    2      3
    3      2
    4      4
          ..
    Name: size, Length: 244, dtype: int64

```

When we run the following code, all values of **each row** will be printed

```python
df.apply(lambda x:print(x), axis=1)
```

```
total_bill     16.99
tip             1.01
sex           Female
smoker            No
day              Sun
time          Dinner
size               2
Name: 0, dtype: object
total_bill     10.34
tip             1.66
sex             Male
smoker            No
day              Sun
time          Dinner
size               3
Name: 1, dtype: object
...
```

Following are different ways to use `apply` function to create new columns in a data frame

```python
def multiply(d):
    # print(d.shape) # (7,)
    d["res"]=d["total_bill"] * d["size"]
    d["res2"]=d["total_bill"] * d["size"]
    return d

def sum(d):
    return d["total_bill"] + d["size"]

df = df.apply(multiply, axis=1)
df["sum"]=df.apply(sum, axis=1)
```


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://gautamnaik1994.gitbook.io/snippets/data-science/pandas/pandas-apply-function.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.