Viterbi algorithm explained using Trellis diagram
INTRODUCTION The Viterbi algorithm is an iterative method used to find the most likely sequence of states according to a pre-defined decision rule related to […]
How to import Anaconda environments in the Jupyter Notebook?
Enter this commands in the Anaconda terminal to add your new environment, here called NewEnv. First install ipykernel (you can do it in the base environment): […]
Pandas.read_csv too slow! (or not…) [Python]
Problem: pandas.read_csv was apparently too slow with a big csv files. In the current version of Pandas (1.0.1) and Python (3.7.3), even by setting the […]
UnicodeDecodeError: ‘utf-8’ codec can’t decode byte 0xb0 in position 12: invalid start byte [Troubleshooting : Python]
ERROR:
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File "pandas\_libs\parsers.pyx", line 529, in pandas._libs.parsers.TextReader.__cinit__ File "pandas\_libs\parsers.pyx", line 749, in pandas._libs.parsers.TextReader._get_header UnicodeDecodeError: 'utf-8' codec can't decode byte 0xb0 in position 12: invalid start byte |
This kind of error happens because of the text encoding. It could be raised by special characters inside the text to be read. […]
Data visualization : bar plot multiple columns against grouped data of Pandas dataframe
We define our initial dataframe and import all necessary libraries with the following sample code:
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import os import pandas as pd import matplotlib.pyplot as plt import matplotlib.cm as cm datapath = os.path.join("..","folder", "test.csv") separator = ";" index_column = "Timestamp" timestamp_format = "%d/%m/%Y %H:%M" df = pd.read_csv(datapath, sep = separator, index_col = [index_column]) df.index = pd.to_datetime(df.index, format=timestamp_format) df = df.sort_index() |
First 10 elements of our dataframe: Now we group […]
Data visualization : how to plot Python Pandas dataframe with different scaled y-axes and legend
Motivation Goal of the following code is to flexibly plot more columns of a dataframe with the following characteristics: datetime values is along the x-axis […]
Sequence shape detection algorithms comparison [Python]
Motivation Goal of this post is to compare some sequence shape comparison criteria. This should be considered as a naive introduction to the topic. Theoretical […]
Performance improvement hints [Python]
If a column, e.g. the timestamp is frequently used to access the DataFrame rows, set it as index. If it should be a datetime format, […]
Useful Pandas code snippets [Python]
Code Snippet #1 Scenario: Data preparation. The initial dataset has string values (also Timestamp). Some of them are written in form of “[number] [unit]” and […]
Some simple and generic Python functions
Filter a dictionary using dictionary comprehension. E.g.:
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{key:val for key, val in unitdict.items() if val == 1} |
Filter Pandas Series only with elements which exist in a dictionary. E.g.:
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myserie[myserie.isin(my_dictionary)] |
Replace values of Pandas […]