["python","functional-programming","pipeline"]
Functional pipes in python like %>% from R's magrittr

In R (thanks to magrittr) you can now perform operations with a more functional piping syntax via %>%. This means that instead of coding this: > as.Date("2014-01-01") > as.character((sqrt(12)^2) You could also do this: > "2014-01-01" %>% as.Date > 12 %>% sqrt %>% .^2 %>% as.character To me this is more readable and this extends to use cases beyond the dataframe. Does the python language have support for something similar?
Pipes are a new feature in Pandas 0.16.2. Example: import pandas as pd from sklearn.datasets import load_iris x = load_iris() x = pd.DataFrame(x.data, columns=x.feature_names) def remove_units(df): df.columns = pd.Index(map(lambda x: x.replace(" (cm)", ""), df.columns)) return df def length_times_width(df): df['sepal length*width'] = df['sepal length'] * df['sepal width'] df['petal length*width'] = df['petal length'] * df['petal width'] x.pipe(remove_units).pipe(length_times_width) x NB: The Pandas version retains Python's reference semantics. That's why length_times_width doesn't need a return value; it modifies x in place.