PythonでPandasを使ったデータ加工やってみた
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　【Python データ加工】GoriPan!【Pandas】
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#Python
#Pandas
#プログラム
#データ加工

A "groupby" call in #Python #Pandas is normally sorted by index.

But if you're grouping by month name, April will come before January.

Pass "sort=False", and the index will reflect the order of appearance:

df.groupby('passenger_count', sort=False)['trip_distance'].mean()

#PANDAS
パンダの監督のアクスタが欲しいと言うフォロワッさんにデータをお渡しして私の分も作ってもらってウレシハズカシテヘリ
色合いがお茶と似ている

Drawing attention with #birthday #pandas; please keep Amparo safe! #mutualaid #helpsky linktr.ee/ayudamutuax

The simplest grouping in #Python #Pandas is groupby:

df.groupby(CATEGORICAL)[NUMERIC].AGG_METHOD()

For example:

df.groupby('passenger_count')['trip_distance'].mean()

Returns a series whose index is the unique values from passenger_count.

Drawing attention with #birthday #pandas; please help Britt to pay off a predatory loan! #mutualaid #helpsky $liminalclown

In #Python #Pandas, dt still works on NaT ("not a time"). You'll get NaN, but that's better than an error:

df['tpep_pickup_datetime'].dt.year # float year (forced by NaN) or NaN
df['tpep_pickup_datetime'].dt.day_name() # string or NaN

These 15 Adorable Photos of Pandas Will Make You Want to Visit the Zoo Immediately: Members of the fluffy and adorable but vulnerable species are often cared for in zoos and breeding centers around the world, and these snapshots may… @SmithsonianMag #Pandas #Zoo #Wildlife #AnimalLovers #CuteAnimals

Python, pandas, Git and testing: what stuck with me

mrneilbryan.substack.com/p/python-pan...

#DataEngineering #Python #pandas #Git #pytest #ContinuousLearning

Performing date arithmetic in #Python #Pandas, and one value is NaT? Then the timedelta will also get a value of NaT.

Remember, timedelta describes a length of time without a set start or end. But NaT works there, too, rather than NaN.

Drawing attention with #birthday #pandas! Please keep Britt and Topaz safe! #mutualaid #helpsky linktr.ee/hiitsbritt

Here are eight cute pictures of red pandas. They look like they were made to be held, then left somewhere soft.
#Eight #Here #Cute #Pandas #Quiet #InternationalNews #Photography #Animals #Wildlife #Nature #PhotoOfTheDay #AnimalPhotography #CuteAnimals #Pictures

In a #Python #Pandas datetime column, you use pd.NaT ("not a time"), not NaN:

- It's pd.NaT, native to Pandas
- It can be stored in datetime columns
- Can appear when text cannot be interpreted as datetime
- It is ignored in aggregations, like NaN
- dropna/fillna handle it, too

How fast are data centers getting built? Where? Is electric capacity growing, too? Who is building them?

These questions are from the latest Bamboo Weekly. Use #Python #Pandas to solve problems based on current events and public data sets.

Learn more: buff.ly/scuO3Dj

Reading a CSV file into #Python #Pandas, and the file uses "-" or "." instead of NaN or a blank cell?

Have Pandas treat those as NaN by passing na_values (and a list of strings) to read_csv:

df = pd.read_csv(filename, na_values=['-', '.'])

Ähtäri cannot face criminal liability over missing panda records #ähtäri #localgovernment #pandas

Drawing attention with #birthday #pandas; please keep Britt and Topaz safe! #MutualAid #HelpSky linktr.ee/hiitsbritt

Replace NaN values in a #Python #Pandas data frame with fillna:

1. Pass it a single value to replace everywhere

df.fillna(999)

2. Pass a series/dict whose keys/index match columns; values fill matching columns.

df.fillna(df.mean())
df.fillna({'x':999, 'y':888, 'z':777})

Drop rows with *only* NaN values in a #Python #Pandas data frame with the how='all' keyword argument:

df.dropna(how='all')

This removes completely empty rows, leaving rows containing even one non-NaN value (aka thresh=1).