Variations of this question have been asked before, I'm still having trouble understanding how to actually slice a python series/pandas dataframe based on conditions that I'd like to set.
In R, what I'm trying to do is:
df[which(df[,colnumber] > somenumberIchoose),]
The which() function finds indices of row entries in a column in the dataframe which are greater than somenumberIchoose, and returns this as a vector. Then, I slice the dataframe by using these row indices to indicate which rows of the dataframe I would like to look at in the new form.
Is there an equivalent way to do this in python? I've seen references to enumerate, which I don't fully understand after reading the documentation. My sample in order to get the row indices right now looks like this:
indexfuture = [ x.index(), x in enumerate(df['colname']) if x > yesterday]
However, I keep on getting an invalid syntax error. I can hack a workaround by for looping through the values, and manually doing the search myself, but that seems extremely non-pythonic and inefficient.
What exactly does enumerate() do? What is the pythonic way of finding indices of values in a vector that fulfill desired parameters?
Note: I'm using Pandas for the dataframes
I may not understand clearly the question, but it looks like the response is easier than what you think:
using pandas DataFrame:
df['colname'] > somenumberIchoose
returns a pandas series with True / False values and the original index of the DataFrame.
Then you can use that boolean series on the original DataFrame and get the subset you are looking for:
df[df['colname'] > somenumberIchoose]
should be enough.
See http://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-indexing
What what I know of R you might be more comfortable working with numpy -- a scientific computing package similar to MATLAB.
If you want the indices of an array who values are divisible by two then the following would work.
arr = numpy.arange(10)
truth_table = arr % 2 == 0
indices = numpy.where(truth_table)
values = arr[indices]
It's also easy to work with multi-dimensional arrays
arr2d = arr.reshape(2,5)
col_indices = numpy.where(arr2d[col_index] % 2 == 0)
col_values = arr2d[col_index, col_indices]
enumerate() returns an iterator that yields an (index, item) tuple in each iteration, so you can't (and don't need to) call .index() again.
Furthermore, your list comprehension syntax is wrong:
indexfuture = [(index, x) for (index, x) in enumerate(df['colname']) if x > yesterday]
Test case:
>>> [(index, x) for (index, x) in enumerate("abcdef") if x > "c"]
[(3, 'd'), (4, 'e'), (5, 'f')]
Of course, you don't need to unpack the tuple:
>>> [tup for tup in enumerate("abcdef") if tup[1] > "c"]
[(3, 'd'), (4, 'e'), (5, 'f')]
unless you're only interested in the indices, in which case you could do something like
>>> [index for (index, x) in enumerate("abcdef") if x > "c"]
[3, 4, 5]
And if you need an additional statement panda.Series allows you to do Operations between Series (+, -, /, , *).
Just multiplicate the indexes:
idx1 = df['lat'] == 49
idx2 = df['lng'] > 15
idx = idx1 * idx2
new_df = df[idx]
Instead of enumerate, I usually just use .iteritems. This saves a .index(). Namely,
[k for k, v in (df['c'] > t).iteritems() if v]
Otherwise, one has to do
df[df['c'] > t].index()
This duplicates the typing of the data frame name, which can be very long and painful to type.
A nice simple and neat way of doing this is the following:
SlicedData1 = df[df.colname>somenumber]]
This can easily be extended to include other criteria, such as non-numeric data:
SlicedData2 = df[(df.colname1>somenumber & df.colname2=='24/08/2018')]
And so on...
来源：https://stackoverflow.com/questions/25086060/pandas-equivalent-of-rs-which