Update pandas_series.md

contrib/pandas/pandas_series.md

@@ -13,14 +13,14 @@ import pandas as pd
 
 s1 = pd.Series([4, 5, 2, 3])
 print(s1)
-
-#Output:
-#0    4
-#1    5
-#2    2
-#3    3
-#dtype: int64
-
+```
+```
+Output:
+0    4
+1    5
+2    2
+3    3
+dtype: int64
 ```
 
 ### Series from a Dictionary
@@ -31,12 +31,13 @@ import pandas as pd
 
 s2 = pd.Series({'A': 1, 'B': 2, 'C': 3})
 print(s2)
-
-#Output:
-#A    1
-#B    2
-#C    3
-#dtype: int64
+```
+```
+Output:
+A    1
+B    2
+C    3
+dtype: int64
 ```
 
 
@@ -50,12 +51,13 @@ import pandas as pd
 
 s4 = pd.Series([1, 2, 3], index=['a', 'b', 'c'], dtype='float64')
 print(s4)
-
-#output
-#a    1.0
-#b    2.0
-#c    3.0
-#dtype: float64
+```
+```
+Output:
+a    1.0
+b    2.0
+c    3.0
+dtype: float64
 ```
 
 ### Specifying NaN Values:
@@ -66,13 +68,13 @@ print(s4)
 import pandas as pd
 s3=pd.Series([1,np.Nan,2])
 print(s3)
-
-#output:
-
-#0  1.0
-#1  NaN
-#2  2.0
-#dtype: float64
+```
+```
+Output:
+0  1.0
+1  NaN
+2  2.0
+dtype: float64
 ```
 
 
@@ -86,15 +88,15 @@ import pandas as pd
 a=np.arange(1,5)  # [1,2,3,4]
 s5=pd.Series(data=a**2,index=a)
 print(s5)
-
-#output:
-#1  1
-#2  4
-#3  9
-#4  16
-#dtype: int64
 ```
-
+```
+Output:
+1  1
+2  4
+3  9
+4  16
+dtype: int64
+```
 
 ## Series Object Attributes
 
@@ -123,9 +125,10 @@ import pandas as pd
 
 s7 = pd.Series(data=[13, 45, 67, 89], index=['A', 'B', 'C', 'D'])
 print(s7['A'])
-
-#output
-#13
+```
+```
+Output:
+13
 ```
 
 
@@ -142,12 +145,14 @@ import pandas as pd
 
 s = pd.Series(data=[13, 45, 67, 89], index=['A', 'B', 'C', 'D'])
 print(s[:2])
+```
+```
+Output:
+A    13
+B    45
+dtype: int64
 
-#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.
 ```
 
 
@@ -165,12 +170,13 @@ s8 = pd.Series([10, 20, 30], index=['a', 'b', 'c'])
 s8['a'] = 100
 s8.index = ['x', 'y', 'z']
 print(s8)
-
-#output
-#x    100
-#y     20
-#z     30
-#dtype: int64
+```
+```
+Output:
+x    100
+y     20
+z     30
+dtype: int64
 ```
 
 **Note: Series object are value-mutable but size immutable objects.**
@@ -183,18 +189,21 @@ import pandas as pd
 s9 = pd.Series([1, 2, 3])
 print("addition:", s9 + 5)
 print("subtraction:", s9 - 2)
+```
+```
+output:
 
-#output:
-#addition: 
-#0    6
-#1    7
-#2    8
-#dtype: int64
-#subtraction: 
-#0   -1
-#1    0
-#2    1
-#dtype: int64
+addition: 
+0    6
+1    7
+2    8
+dtype: int64
+
+subtraction: 
+0   -1
+1    0
+2    1
+dtype: int64
 ```
 
 ### Arthmetic on series object
@@ -206,18 +215,21 @@ s11 = pd.Series([4, 5, 6])
 print("addition:", s10 + s11)
 
 print("multiplication:", s10 * s11)
+```
+```
+output:
 
-#output:
-#addition: 
-#0    5
-#1    7
-#2    9
-#dtype: int64
-#multiplication: 
-#0     4
-#1    10
-#2    18
-#dtype: int64
+addition: 
+0    5
+1    7
+2    9
+dtype: int64
+
+multiplication: 
+0     4
+1    10
+2    18
+dtype: int64
 ```
 
 Here one thing we should keep in mind that both the series object should have same indexes otherwise it will return NaN value to all the indexes of two series object .
@@ -236,17 +248,18 @@ import pandas as pd
 s12 = pd.Series([10, 20, 30, 40, 50, 60, 70, 80, 90, 100])
 print(s12.head(3))
 print(s12.tail(3))
+```
+```
+Output:
+0    10
+1    20
+2    30
+dtype: int64
 
-#output
-#0    10
-#1    20
-#2    30
-#dtype: int64
-
-#7     80
-#8     90
-#9    100
-#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`.
 
@@ -263,20 +276,22 @@ s13 = pd.Series([3, 1, 2], index=['c', 'a', 'b'])
 print(s13.sort_values())
 print(s13.sort_index())
 print(s13.drop('a'))
-#Output
-#a    1
-#b    2
-#c    3
-#dtype: int64
+```
+```
+Output:
+a    1
+b    2
+c    3
+dtype: int64
 
-#a    1
-#b    2
-#c    3
-#dtype: int64
+a    1
+b    2
+c    3
+dtype: int64
 
-#c    3
-#b    2
-#dtype: int64
+c    3
+b    2
+dtype: int64
 ```
 
 ## Conclusion