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+---
+Title: '.prod()'
+Description: 'Returns the first non-null value from each group.'
+Subjects:
+  - 'Computer Science'
+  - 'Data Science'
+Tags:
+  - 'Data Structures'
+  - 'Pandas'
+CatalogContent:
+  - 'learn-python-3'
+  - 'paths/data-science'
+---
+
+The **`groupby().first()`** method in pandas returns the first non-null value from each group created by a `groupby()` operation. It is commonly used to extract representative or initial entries for grouped data, such as the first transaction, first record, or earliest occurrence.
+
+## Syntax
+
+```pseudo
+DataFrameGroupBy.first(numeric_only=False, min_count=-1, skipna=True)
+```
+
+**Parameters:**
+
+- `numeric_only` (bool, default `False`): Includes only float, int, and boolean columns in the operation.
+- `min_count` (int, default `-1`): Minimum number of valid values required to perform the operation. If fewer values are available, returns `NaN`.
+- `skipna` (bool, default `True`): Excludes `NA`/`null` values. If all entries are `NA`, the result is `NA`.
+
+**Return value:**
+
+Returns a Series or DataFrame. First non-null values within each group.
+
+## Example 1
+
+In this example, the method retrieves the first entry of each department from the DataFrame:
+
+```py
+import pandas as pd
+
+data = {
+  'Department': ['HR', 'Finance', 'HR', 'IT', 'Finance', 'IT'],
+  'Employee': ['Alice', 'Bob', 'Charlie', 'David', 'Eva', 'Frank'],
+  'Salary': [50000, 60000, 52000, 58000, 61000, 59000]
+}
+
+df = pd.DataFrame(data)
+result = df.groupby('Department').first()
+print(result)
+```
+
+The output of this code is:
+
+```shell
+           Employee  Salary
+Department
+Finance         Bob   60000
+HR             Alice   50000
+IT             David   58000
+```
+
+## Example 2
+
+In this example, the method extracts the first available record for each city group while skipping missing values:
+
+```py
+import pandas as pd
+import numpy as np
+
+data = {
+  'City': ['Delhi', 'Delhi', 'Mumbai', 'Mumbai', 'Chennai', 'Chennai'],
+  'Temperature': [32, np.nan, 30, 29, np.nan, 27],
+  'Humidity': [80, 82, 75, 78, 70, 72]
+}
+
+df = pd.DataFrame(data)
+result = df.groupby('City').first()
+print(result)
+```
+
+The output of this code is:
+
+```shell
+         Temperature  Humidity
+City
+Chennai         NaN        70
+Delhi          32.0        80
+Mumbai         30.0        75
+```
+
+## Codebyte Example
+
+In this example, the method finds the first purchase record for each customer, showing how it can be applied to analyze grouped transaction data:
+
+```codebyte/python
+import pandas as pd
+
+data = {
+  'Customer': ['Alice', 'Alice', 'Bob', 'Charlie', 'Charlie', 'Charlie'],
+  'Purchase': ['Shoes', 'Bag', 'Watch', 'Shirt', 'Shoes', 'Hat'],
+  'Amount': [120, 80, 150, 200, 90, 50]
+}
+
+df = pd.DataFrame(data)
+result = df.groupby('Customer').first()
+print(result)
+```
+
+## Frequently Asked Questions
+
+### 1. What does groupby `first()` do?
+
+It returns the first non-null record from each group in a grouped pandas object.
+
+### 2. How do you get the first in a pandas group?
+
+By applying the `first()` method after using `groupby()`, such as `df.groupby('Column').first()`.
+
+### 3. How to print the first 10 rows of a pandas DataFrame?
+
+Use the `head(10)` method, which displays the first 10 rows of the DataFrame.