diff --git a/.travis.yml b/.travis.yml
index f913f183..c76284b3 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -20,7 +20,7 @@ jobs:
       env: PATH=/c/Python38:/c/Python38/Scripts:$PATH
 install: 
     - pip3 install --upgrade pip  # all three OSes agree about 'pip3'
-    - pip3 install black
+    - pip3 install black==19.10b0
     - pip3 install ".[dev]" .
 # 'python' points to Python 2.7 on macOS but points to Python 3.8 on Linux and Windows
 # 'python3' is a 'command not found' error on Windows but 'py' works on Windows only
diff --git a/setup.cfg b/setup.cfg
index d6103b02..3f86e7f3 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -41,7 +41,7 @@ install_requires =
 # TODO pick the correct version.
 [options.extras_require]
 dev =
-    black>=19.10b0
+    black==19.10b0
     pytest>=4.0.0
     Sphinx>=3.0.3
     sphinx-markdown-builder>=0.5.4
diff --git a/website/docs/assets/wordcloud.png b/website/docs/assets/wordcloud.png
new file mode 100644
index 00000000..4ae51681
Binary files /dev/null and b/website/docs/assets/wordcloud.png differ
diff --git a/website/docs/getting-started-visulization.md b/website/docs/getting-started-visulization.md
new file mode 100644
index 00000000..32bf2ddb
--- /dev/null
+++ b/website/docs/getting-started-visulization.md
@@ -0,0 +1,60 @@
+---
+id: getting-started-visualization
+title: Getting started - Visualization
+---
+
+# Visualization
+
+Now, before you start processing your dataset, you might want to visualize your data first in order to get the gist of the data and to choose, which NLP toolkit and models will be most suitable. The following tutorial will introduce two methods. Those will show you in a quick way the most frequent words in our dataset.
+
+## 1. Top words
+
+
+The easiest way to find out the most important, is, to have a look at
+their absolute occurence in the set. This is simply how often a word/token occurs in your set. Before Texthero that easy task was quite complex to program. You first needed to write your own tokenizer, generate a DocumentTerm matrix with the CountVectorizer for example, then sum over one axis and sort in the end. This process is now simplyfied by Texthero.
+
+```python
+>>> # save the dataset in df
+>>> import texthero as hero
+>>> import pandas as pd
+>>> df = pd.read_csv("https://github.com/jbesomi/texthero/raw/master/dataset/bbcsport.csv")
+>>> # now we will extract all top words
+>>> top_words = hero.top_words(df["text"])
+>>> top_words.head()
+the 12790
+to 7051
+a 5516
+in 5271
+and 5259
+Name: text, dtype: int64
+```
+
+However, we can now see, that from the most common words we don't get so much information, as we have hoped for. This is, that the english language does not only contain relevant words with information, but also stopwords, which purpose is, to connect important words to gramaticaly complete sentences. To extract the most relevant parts of the texts, we will now first clean it with the texthero `clean` function and then look again at the topwords.
+
+```python
+>>> df["clean"] = hero.clean(df["text"])
+>>> top_words = hero.top_words(df["clean"])
+said 1338
+first 790
+england 749
+game 681
+one 671
+Name: clean, dtype: int64
+```
+
+Now we can see, that for example most of the texts contain "england" and are about "games". That is now quite useful for further analysis and can be done in just two lines of code.
+
+## 2. Wordcloud
+
+But the data frame is still quite technical and less graphical. This can be improved by generating a WordCloud. Texthero has a build-in function, which calls the word_cloud package API to generate the picture. A wordcloud consits of the top words in our dataset arranged in a cloud, where the more frequent words as visualised bigger than the less frequent ones. When executing the following lines in a jupyter notebook, it will show you a wordcloud with the most common words
+```python
+>>> import texthero as hero
+>>> import pandas as pd
+>>> df = pd.read_csv("https://github.com/jbesomi/texthero/raw/master/dataset/bbcsport.csv")
+>>> df["clean"] = hero.clean(df["text"])
+>>> hero.wordcloud(df["clean"])
+```
+
+![](/img/wordcloud.png)
+
+Here we can easily recognise the popular words from before, as they are printed bigger than the others.
diff --git a/website/sidebars.json b/website/sidebars.json
index bca3356b..e8f9a647 100644
--- a/website/sidebars.json
+++ b/website/sidebars.json
@@ -1,7 +1,8 @@
 {
   "docs": {
     "Getting Started": [
-      "getting-started"
+      "getting-started",
+      "getting-started-visualization"
     ]
   },
   "api": {