basicpython

pandas

In creating functions for pd.DataFrame.pipe, start with a copy of the DataFrame.

df.where(lambda df: df % 3 == 0, -df-2) # note the callable that computes where to replace
assign (0.16.0): For adding new columns to a DataFrame in a chain (inspired by dplyr's mutate)
pipe (0.16.2): For including user-defined methods in method chains.
rename (0.18.0): For altering axis names (in additional to changing the actual labels as before).
Window methods (0.18): Took the top-level pd.rolling_* and pd.expanding_* functions and made them NDFrame methods with a groupby-like API.
Resample (0.18.0) Added a new groupby-like API
.where/mask/Indexers accept Callables (0.18.1): In the next release you'll be able to pass a callable to the indexing methods, to be evaluated within the DataFrame's context (like .query, but with code instead of strings).
pd.DataFrame.equals, for comparing data frames (having NaN in the same place compares as true)
dropna
index operations: & intersection, | union, and ^ symmetric difference

logging.basicConfig needs to come before first call that uses it. Otherwise will be ignored.

logging.basicConfig(level=logging.INFO)

Use PyYaml through import yaml.

Printing without having {c: 4} as value of b:

yaml.dump({'a': {'b': {'c' :4}}}, default_flow_style=False)

Reading a yaml file

with open("logging_1.yaml") as f:
    dd = yaml.load(f)

Use yaml.load_all if the input contains multiple yaml documents separated by ---

Note can deal with Pythhon objects (writing and reading through tags)