minor changes

docs/MapReduce.md

@@ -12,7 +12,7 @@ In the first example, we will implement word count.
 The mapper is defined as following: 
 
 ```py title="map.py"
-!/usr/bin/env python3
+#!/usr/bin/env python3
 import sys
 import re
 

docs/Spark.md

@@ -205,9 +205,12 @@ Then we initialize the PageRank od each player.
 Nodes = (xs.keys() + xs.values()).distinct()
 ranks = Nodes.map(lambda x: (x, 100))
 sorted(ranks.collect())
---------------------------
-[('player1', 1.0), ('player2', 1.0), ('player3', 1.0), ('player4', 1.0)]
 
+```
+
+```
+--------------------------
+[('player1', 100.0), ('player2', 100.0), ('player3', 100.0), ('player4', 100.0)]
 ```
 Here we join lists of lost games and PageRank into tupple. As links and ranks have same keys, we can use that by calling ```join()``` function. 
 ```python 
@@ -436,7 +439,6 @@ only showing top 20 rows
 df.filter(df['species']=='setosa').show()
 
 ```
-
 ```
 +------------+-----------+------------+-----------+-------+
 |sepal_length|sepal_width|petal_length|petal_width|species|
@@ -463,6 +465,7 @@ df.filter(df['species']=='setosa').show()
 |         5.1|        3.8|         1.5|        0.3| setosa|
 +------------+-----------+------------+-----------+-------+
 only showing top 20 rows
+
 ```
 
 ```py
@@ -471,44 +474,27 @@ df.groupBy('species').sum().show()
 ```
 
 ```
-+------------+-----------+------------+-----------+-------+
-|sepal_length|sepal_width|petal_length|petal_width|species|
-+------------+-----------+------------+-----------+-------+
-|         5.1|        3.5|         1.4|        0.2| setosa|
-|         4.9|        3.0|         1.4|        0.2| setosa|
-|         4.7|        3.2|         1.3|        0.2| setosa|
-|         4.6|        3.1|         1.5|        0.2| setosa|
-|         5.0|        3.6|         1.4|        0.2| setosa|
-|         5.4|        3.9|         1.7|        0.4| setosa|
-|         4.6|        3.4|         1.4|        0.3| setosa|
-|         5.0|        3.4|         1.5|        0.2| setosa|
-|         4.4|        2.9|         1.4|        0.2| setosa|
-|         4.9|        3.1|         1.5|        0.1| setosa|
-|         5.4|        3.7|         1.5|        0.2| setosa|
-|         4.8|        3.4|         1.6|        0.2| setosa|
-|         4.8|        3.0|         1.4|        0.1| setosa|
-|         4.3|        3.0|         1.1|        0.1| setosa|
-|         5.8|        4.0|         1.2|        0.2| setosa|
-|         5.7|        4.4|         1.5|        0.4| setosa|
-|         5.4|        3.9|         1.3|        0.4| setosa|
-|         5.1|        3.5|         1.4|        0.3| setosa|
-|         5.7|        3.8|         1.7|        0.3| setosa|
-|         5.1|        3.8|         1.5|        0.3| setosa|
-+------------+-----------+------------+-----------+-------+
-only showing top 20 rows
++----------+------------------+------------------+------------------+------------------+
+|   species| sum(sepal_length)|  sum(sepal_width)| sum(petal_length)|  sum(petal_width)|
++----------+------------------+------------------+------------------+------------------+
+| virginica| 329.3999999999999|             148.7|277.59999999999997|101.29999999999998|
+|versicolor|             296.8|138.50000000000003|212.99999999999997|              66.3|
+|    setosa|250.29999999999998|170.90000000000003|              73.2|12.199999999999996|
++----------+------------------+------------------+------------------+------------------+
 ```
 ```py
 df.groupBy('species').max().show()
 ```
 
 ```
-+----------+-----------------+----------------+-----------------+----------------+
+++----------+-----------------+----------------+-----------------+----------------+
 |   species|max(sepal_length)|max(sepal_width)|max(petal_length)|max(petal_width)|
 +----------+-----------------+----------------+-----------------+----------------+
 | virginica|              7.9|             3.8|              6.9|             2.5|
 |versicolor|              7.0|             3.4|              5.1|             1.8|
 |    setosa|              5.8|             4.4|              1.9|             0.6|
 +----------+-----------------+----------------+-----------------+----------------+
+
 ```
 **Clustering**
 

docs/tutorial.md

@@ -18,7 +18,7 @@ The dataset contains `.csv files` with WTA matches from 1968 until 2023.
 
 
 
-## Requirement
+## Requirements
 
 - python3
 - mrjob ```pip install mrjob```