Update viewing_data_in_pandas.md

contrib/pandas/viewing_data_in_pandas.md

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-**View the top rows of the frame**
-**Pandas Dataframe/Series.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:
+# View the top rows of the frame 
 
-*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.
-** Example**
+# Pandas Dataframe/Series.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**:
+
+     dataframe.head(n)
+     
+ n is the Optional value. The number of rows to return. Default value is 5.
+
+**Example** :
+  
+     import pandas as pd
+     df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion','tiger','rabit','dog','fox','monkey','elephant']})
+     df.head(n=5)
+
+**Output**:
+
+'alligator',
+'bee', 
+'falcon', 
+'lion',
+'tiger'
+# View the bottom rows of the frame 
+
+# Pandas Dataframe/Series.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**:
+
+     dataframe.tail(n)
+     
+ n is the Optional value. The number of rows to return. Default value is 5.
+
+ **Example** :
+  
+     import pandas as pd
+     df = pd.DataFrame({'fruits': ['mongo', 'orange', 'apple', 'lemon','banana','water melon','papaya','grapes','cherry','coconut']})
+     df.tail(n=5)
+
+**Output**:
+
+
+'water melon',
+'papaya',
+'grapes',
+'cherry',
+'coconut'
+# View basic statistical details
+
+# Pandas DataFrame describe() Method
+
+Pandas describe() is used to view some basic statistical details like percentile, mean, std, etc. of a data frame or a series of numeric values.Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.
+
+Analyzes both numeric and object series, as well as DataFrame column sets of mixed data types.The output will vary depending on what is provided.
+
+**Syntax**:
+
+    DataFrame.describe(percentiles=None, include=None, exclude=None)
+**percentiles** : list-like of numbers, optional
+
+The percentiles to include in the output. All should fall between 0 and 1. The default is [.25, .5, .75], which returns the 25th, 50th, and 75th percentiles. 
+
+**include** :‘all’, list-like of dtypes or None (default), optional
+
+A  list of data types to include in the result. 
+* all’ : All columns of the input will be included in the output.
+
+* A list-like of dtypes : Limits the results to the provided data types. To limit the result to numeric types submit numpy.number. To select pandas categorical columns, use 'category'
+
+* None (default) : The result will include all numeric columns.
+
+**exclude** : list-like of dtypes or None (default), optional.
+
+A black list of data types to omit from the result. 
+* A list-like of dtypes : Excludes the provided data types from the result. To exclude numeric types submit numpy.number. To exclude object columns submit the data type numpy.object.
+
+* None (default) : The result will exclude nothing.
+
+**Example** :
+
+Describing a numeric Series.
+
+        import pandas as pd
+        s = pd.Series([1, 2, 3])
+        s.describe()
+        
+**Output** :
+
+count    3.0
+
+mean     2.0
+
+std      1.0
+
+min      1.0
+
+25%      1.5
+
+50%      2.0
+
+75%      2.5
+
+max      3.0
+
+dtype: float64
+
+**Example** :
+
+Describing a categorical Series.
+
+
+         import pandas as pd
+         s = pd.Series(['a', 'a', 'b', 'c'])
+         s.describe()
+
+**Output** :
+
+count     4
+
+unique    3
+
+top       a
+
+freq      2
+
+dtype: object
+
+
+        
+
+      
+
+
+
+
+
+
+
+
+
+  
+