Update and rename pandas_series.md to pandas-series.md

contrib/pandas/pandas-series.md

@@ -1,7 +1,6 @@
 # Pandas Series
 
-
+A series is a Panda data structures that represents a one dimensional array-like object containing an array of data and an associated array of data type labels, called index.
- A series is a Panda data structures that represents a one dimensional array-like object containing an array of data and an associated array of data type labels, called index.
 
 ## Creating a Series object:
 
@@ -14,8 +13,9 @@ import pandas as pd
 s1 = pd.Series([4, 5, 2, 3])
 print(s1)
 ```
+
+#### Output
 ```
-Output:
 0    4
 1    5
 2    2
@@ -32,8 +32,9 @@ import pandas as pd
 s2 = pd.Series({'A': 1, 'B': 2, 'C': 3})
 print(s2)
 ```
+
+#### Output
 ```
-Output:
 A    1
 B    2
 C    3
@@ -52,8 +53,9 @@ import pandas as pd
 s4 = pd.Series([1, 2, 3], index=['a', 'b', 'c'], dtype='float64')
 print(s4)
 ```
+
+#### Output
 ```
-Output:
 a    1.0
 b    2.0
 c    3.0
@@ -69,8 +71,9 @@ import pandas as pd
 s3=pd.Series([1,np.Nan,2])
 print(s3)
 ```
+
+#### Output
 ```
-Output:
 0  1.0
 1  NaN
 2  2.0
@@ -89,8 +92,9 @@ a=np.arange(1,5)  # [1,2,3,4]
 s5=pd.Series(data=a**2,index=a)
 print(s5)
 ```
+
+#### Output
 ```
-Output:
 1  1
 2  4
 3  9
@@ -111,8 +115,6 @@ dtype: int64
 | `<series>.hasnans`       | Return True if there is any NaN in the data       |
 | `<series>.empty`         | Return True if the Series object is empty         |
 
-
-
 - If you use len() on a series object then it return total number of elements in the series object whereas <series_object>.count() return only the number of non NaN elements.
 
 ##  Accessing a Series object and its elements
@@ -126,12 +128,12 @@ import pandas as pd
 s7 = pd.Series(data=[13, 45, 67, 89], index=['A', 'B', 'C', 'D'])
 print(s7['A'])
 ```
+
+#### Output
 ```
-Output:
 13
 ```
 
-
 ### Slicing a Series
 
 - Slices are extracted based on their positional index, regardless of the custom index labels.
@@ -146,15 +148,15 @@ import pandas as pd
 s = pd.Series(data=[13, 45, 67, 89], index=['A', 'B', 'C', 'D'])
 print(s[:2])
 ```
+
+#### Output
 ```
-Output:
 A    13
 B    45
 dtype: int64
-
-This example demonstrates that the first two elements (positions 0 and 1) are returned, regardless of their custom index labels.
 ```
 
+This example demonstrates that the first two elements (positions 0 and 1) are returned, regardless of their custom index labels.
 
 ## Operation on series object
 
@@ -171,8 +173,9 @@ s8['a'] = 100
 s8.index = ['x', 'y', 'z']
 print(s8)
 ```
+
+#### Output
 ```
-Output:
 x    100
 y     20
 z     30
@@ -181,25 +184,32 @@ dtype: int64
 
 **Note: Series object are value-mutable but size immutable objects.**
 
-### vector operations
+### Vector operations
 We can perform vector operations such as `+`,`-`,`/`,`%` etc.
+
+#### Addition
 ```python
 import pandas as pd
 
 s9 = pd.Series([1, 2, 3])
-print("addition:", s9 + 5)
+print(s9 + 5)
-print("subtraction:", s9 - 2)
 ```
-```
-output:
 
-addition: 
+#### Output
+```
 0    6
 1    7
 2    8
 dtype: int64
+```
 
-subtraction: 
+#### Subtraction
+```python
+print(s9 - 2)
+```
+
+#### Output
+```
 0   -1
 1    0
 2    1
@@ -207,25 +217,32 @@ dtype: int64
 ```
 
 ### Arthmetic on series object
+
+#### Addition
 ```python
 import pandas as pd
 
 s10 = pd.Series([1, 2, 3])
 s11 = pd.Series([4, 5, 6])
-print("addition:", s10 + s11)
+print(s10 + s11)
-
-print("multiplication:", s10 * s11)
 ```
-```
-output:
 
-addition: 
+#### Output
+```
 0    5
 1    7
 2    9
 dtype: int64
+```
 
-multiplication: 
+#### Multiplication
+
+```python
+print("s10 * s11)
+```
+
+#### Output
+```
 0     4
 1    10
 2    18
@@ -249,26 +266,28 @@ s12 = pd.Series([10, 20, 30, 40, 50, 60, 70, 80, 90, 100])
 print(s12.head(3))
 print(s12.tail(3))
 ```
+
+#### Output
 ```
-Output:
 0    10
 1    20
 2    30
 dtype: int64
-
 7     80
 8     90
 9    100
 dtype: int64
 ```
+
 If you dont provide any value to n the by default it give results for `n=5`.
 
-### Few extra functions:
+### Few extra functions
 | **Function**                           | **Description**                                                        |
 |----------------------------------------|------------------------------------------------------------------------|
 | `<series_object>.sort_values()`        | Return the Series object in ascending order based on its values.       |
 | `<series_object>.sort_index()`         | Return the Series object in ascending order based on its index.        |
 | `<series_object>.sort_drop(<index>)`   | Return the Series with the deleted index and its corresponding value.  |
+
 ```python
 import pandas as pd
 
@@ -277,22 +296,21 @@ print(s13.sort_values())
 print(s13.sort_index())
 print(s13.drop('a'))
 ```
+
+#### Output
 ```
-Output:
 a    1
 b    2
 c    3
 dtype: int64
-
 a    1
 b    2
 c    3
 dtype: int64
-
 c    3
 b    2
 dtype: int64
 ```
 
 ## Conclusion 
-In short, Pandas Series is a fundamental data structure in Python for handling one-dimensional data. It combines an array of values with an index, offering efficient methods for data manipulation and analysis. With its ease of use and powerful functionality, Pandas Series is widely used in data science and analytics for tasks such as data cleaning, exploration, and visualization.
+In short, Pandas Series is a fundamental data structure in Python for handling one-dimensional data. It combines an array of values with an index, offering efficient methods for data manipulation and analysis. With its ease of use and powerful functionality, Pandas Series is widely used in data science and analytics for tasks such as data cleaning, exploration, and visualization.