diff --git a/contrib/pandas/index.md b/contrib/pandas/index.md
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@@ -1,6 +1,7 @@
 # List of sections
 
 - [Pandas Introduction and Dataframes in Pandas](introduction.md)
+- [Viewing data in pandas](viewing-data.md)
 - [Pandas Series Vs NumPy ndarray](pandas-series-vs-numpy-ndarray.md)
 - [Pandas Descriptive Statistics](descriptive-statistics.md)
 - [Group By Functions with Pandas](groupby-functions.md)
diff --git a/contrib/pandas/viewing-data.md b/contrib/pandas/viewing-data.md
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+# Viewing rows of the frame 
+
+## `head()` method
+
+The pandas library in Python provides a convenient method called `head()` that allows you to view the first few rows of a DataFrame. Let me explain how it works:
+- The `head()` function returns the first n rows of a DataFrame or Series.
+- By default, it displays the first 5 rows, but you can specify a different number of rows using the n parameter.
+
+### Syntax
+
+```python
+dataframe.head(n)
+```
+     
+`n` is the Optional value. The number of rows to return. Default value is `5`.
+
+### Example
+
+```python
+import pandas as pd
+df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion','tiger','rabit','dog','fox','monkey','elephant']})
+df.head(n=5)
+```
+
+#### Output
+
+```
+      animal
+0  alligator
+1        bee
+2     falcon
+3       lion
+4      tiger
+```
+
+## `tail()` method
+
+The `tail()` function in Python displays the last five rows of the dataframe by default. It takes in a single parameter: the number of rows. We can use this parameter to display the number of rows of our choice.
+- The `tail()` function returns the last n rows of a DataFrame or Series.
+- By default, it displays the last 5 rows, but you can specify a different number of rows using the n parameter.
+
+### Syntax
+
+```python
+dataframe.tail(n)
+```
+
+`n` is the Optional value. The number of rows to return. Default value is `5`.
+
+### Example
+
+```python
+import pandas as pd
+df = pd.DataFrame({'fruits': ['mongo', 'orange', 'apple', 'lemon','banana','water melon','papaya','grapes','cherry','coconut']})
+df.tail(n=5)
+```
+
+#### Output
+
+```
+        fruits
+5  water melon
+6       papaya
+7       grapes
+8       cherry
+9      coconut
+```