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
Metpy.plots USCONTIES
`#91 - Temperature Calculations and Pandas
#metpy , unit conversion, apparent temperature, wind chill
`#93 - Pandas and Datetime Indexes
`#94 - Pandas Column Cleanup
`#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
def function(row):
var=row["column"]
... = var ....
df["new"]=df.apply(function,axis=1)

Tips
Get temperature array from csv of lon, lat, temp
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!

Subpages

Related Libraries
bamboolib a GUI for pandas

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