Programming and frameworks for ML Python for data analyis
[Pages:172]Programming and frameworks for ML Python for data analyis
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About Me
Big Data Consultant at Santander / Big Data Lecturer More than 20 years of experience in different environments,
technologies, customers, countries ... Passionate about data and technology Enthusiastic about Big Data world and NoSQL
daniel.villanueva@immune.institute
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Agenda
Reading / Writing data Exploring a DataFrame Renaming headings Filtering Columns Filtering Rows Sorting Data Changing Data Deleting Data Grouping Data Concatenating Data Joining Data Pivot Tables
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Reading / Writing data
Pandas supports the integration with many file formats or data sources out of the box (csv, excel, sql, json, parquet,...).
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Reading data in text format
The function pd.read_csv() allows you to read a file and store it in a DataFrame
With the default options, files must have a header (first row) and the separator is a comma
The file could be both on disk and on the network The file can be compressed!
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Reading data in text format
Sometimes the separator is something different to a comma, for example a pipe (|).
Use the sep argument to change the separator
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Reading data in text format
We can use the header parameter to tell pandas where the header is located (None if the data don't have a header)
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Reading data in text format
If the file don't have a header we can specify the columns' names with the names parameter
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