From c4552e04e1271a9210a934233beae5be1943d034 Mon Sep 17 00:00:00 2001
From: Théo de la Hogue
Date: Wed, 7 Jun 2023 14:34:14 +0200
Subject: Writing User guide and use cases section.

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 .../pandas_dataframe_conversion.md                 | 31 ++++++++++++++++++++++
 1 file changed, 31 insertions(+)
 create mode 100644 docs/user_guide/timestamped_data/pandas_dataframe_conversion.md

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diff --git a/docs/user_guide/timestamped_data/pandas_dataframe_conversion.md b/docs/user_guide/timestamped_data/pandas_dataframe_conversion.md
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+Pandas dataframe conversion
+===========================
+
+A [Pandas dataframe](https://pandas.pydata.org/docs/getting_started/intro_tutorials/01_table_oriented.html#min-tut-01-tableoriented) is a python data structure allowing powerful table processings.
+
+[TimeStampedBuffer](/argaze/#argaze.DataStructures.TimeStampedBuffer) instance can be converted into dataframe provided that data values are stored as dictionaries.
+
+```python
+from argaze import DataStructures
+
+# Create a timestamped data buffer
+ts_data = DataStructures.TimeStampedBuffer()
+
+# Store various data as dictionary
+ts_data[10] = {"A_key": 0, "B_key": 0.123}}
+ts_data[20] = {"A_key": 4, "B_key": 0.567}}
+ts_data[30] = {"A_key": 8, "B_key": 0.901}}
+...
+
+# Convert timestamped data buffer into dataframe
+ts_buffer_dataframe = ts_buffer.as_dataframe()
+```
+
+ts_buffer_dataframe would look like:
+
+|timestamp|A_key|B_key|
+|:--------|:----|:----|
+|10       |0    |0.123|
+|20       |4    |0.567|
+|30       |8    |0.901|
+|...      |...  |...  |
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