From 1fda03516f4fcc1f594a3e516fc6260546d88ae0 Mon Sep 17 00:00:00 2001
From: extremq <45830561+extremq@users.noreply.github.com>
Date: Thu, 13 Apr 2023 12:06:50 +0300
Subject: [PATCH] Fix typos in course.

---
 2-Working-With-Data/07-python/README.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/2-Working-With-Data/07-python/README.md b/2-Working-With-Data/07-python/README.md
index 119b53c..e7ab2e2 100644
--- a/2-Working-With-Data/07-python/README.md
+++ b/2-Working-With-Data/07-python/README.md
@@ -97,6 +97,7 @@ b = pd.Series(["I","like","to","play","games","and","will","not","change"],index
 df = pd.DataFrame([a,b])
 ```
 This will create a horizontal table like this:
+
 |     | 0   | 1    | 2   | 3   | 4      | 5   | 6      | 7    | 8    |
 | --- | --- | ---- | --- | --- | ------ | --- | ------ | ---- | ---- |
 | 0   | 1   | 2    | 3   | 4   | 5      | 6   | 7      | 8    | 9    |
@@ -122,7 +123,7 @@ This will give us a table like this:
 
 **Note** that we can also get this table layout by transposing the previous table, eg. by writing 
 ```python
-df = pd.DataFrame([a,b]).T..rename(columns={ 0 : 'A', 1 : 'B' })
+df = pd.DataFrame([a,b]).T.rename(columns={ 0 : 'A', 1 : 'B' })
 ```
 Here `.T` means the operation of transposing the DataFrame, i.e. changing rows and columns, and `rename` operation allows us to rename columns to match the previous example.