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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 deleted file mode 100644 index 7614e73..0000000 --- a/docs/user_guide/timestamped_data/pandas_dataframe_conversion.md +++ /dev/null @@ -1,41 +0,0 @@ ---- -title: Pandas DataFrame conversion ---- - -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. - -## Export as dataframe - -[TimeStampedBuffer](../../argaze.md/#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| -|... |... |... | - -## Import from dataframe - -Reversely, [TimeStampedBuffer](../../argaze.md/#argaze.DataStructures.TimeStampedBuffer) instance can be created from dataframe, as a result of which each dataframe columns label will become a key of data value dictionary. Notice that the column containing timestamp values have to be called 'timestamp'. |