pandas
pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
Tutorials
`#57 - Pandas CSV and Mapping : Unidata Developer's Blog
read_csv
`#91 - Temperature Calculations and Pandas #metpy , unit conversion, apparent temperature, wind chill `#93 - Pandas and Datetime Indexes `#96 - Pandas Multi-Index Dataframes `#97 - Pandas Concatenation
`#98 - Pandas Merge/Join
`#99 - Pandas Replace and Groupby
`#169 - Saving Space with Pandas - The One Keyword Argument you are Missing : Unidata Developer's Blog
`#181 - Using Apply to Speed Up Pandas DataFrame Operations
code:python
def function(row):
... = var ....
df"new"=df.apply(function,axis=1) Tips
Get temperature array from csv of lon, lat, temp
code:python
table=pd.pivot_table(output, values='temp', index='lat', columns='lon') temp_array=table.values
Everything You Need to Know About “loc” and “iloc” of Pandas | by Soner Yıldırım | Towards Data Science
Pandas (Python): Use of .loc and .iloc | by Maurizio Sluijmers | Jun, 2020 | Level Up Coding
Missing data can throw off everything from your calculations crashing to feeding you incorrect results to interpret. The week we start a short series on ways to deal with missing data in Python.
What's the difference between np.nan and pd.NA? When do we use them? Find out in this week's MetPy Monday!
How can you use filling and interpolation to deal with missing data? Find out in this week's MetPy Monday!
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