Fix Python – Why were pandas merges in python faster than data.table merges in R in 2012?

I recently came across the pandas library for python, which according to this benchmark performs very fast in-memory merges. It’s even faster than the data.table package in R (my language of choice for analysis).
Why is pandas so much faster than data.table? Is it because of an inherent speed advantage python has over R, or is there some tradeof….

Fix Python – What is the difference between join and merge in Pandas?

Suppose I have two DataFrames like so:
left = pd.DataFrame({‘key1’: [‘foo’, ‘bar’], ‘lval’: [1, 2]})

right = pd.DataFrame({‘key2’: [‘foo’, ‘bar’], ‘rval’: [4, 5]})

I want to merge them, so I try something like this:
pd.merge(left, right, left_on=’key1′, right_on=’key2′)

And I’m happy
key1 lval key2 rval
0 foo 1 foo ….

Fix Python – pandas three-way joining multiple dataframes on columns

I have 3 CSV files. Each has the first column as the (string) names of people, while all the other columns in each dataframe are attributes of that person.
How can I “join” together all three CSV documents to create a single CSV with each row having all the attributes for each unique value of the person’s string name?
The join() function in panda….

Fix Python – pandas: merge (join) two data frames on multiple columns

I am trying to join two pandas data frames using two columns:
new_df = pd.merge(A_df, B_df, how=’left’, left_on='[A_c1,c2]’, right_on = ‘[B_c1,c2]’)

but got the following error:
pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4164)()

pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:4028)()


Fix Python – Pandas Merging 101

How can I perform a (INNER| (LEFT|RIGHT|FULL) OUTER) JOIN with pandas?
How do I add NaNs for missing rows after a merge?
How do I get rid of NaNs after merging?
Can I merge on the index?
How do I merge multiple DataFrames?
Cross join with pandas
merge? join? concat? update? Who? What? Why?!

… and more. I’ve seen these recurring questions askin….