2017 Answer – pandas 0.20: .ix is deprecated. Use .loc See the deprecation in the docs .loc uses label based indexing to select both rows and columns. The labels being the values of the index or the columns. Slicing with .loc includes the last element. Let’s assume we have a DataFrame with the following columns:foo, bar, quz, ant, cat, sat, dat. .loc accepts the same slice … Read more
It specifies the axis along which the means are computed. By default axis=0. This is consistent with the numpy.mean usage when axis is specified explicitly (in numpy.mean, axis==None by default, which computes the mean value over the flattened array) , in which axis=0 along the rows (namely, index in pandas), and axis=1 along the columns. For added clarity, one may choose to specify axis=’index’ (instead of axis=0) or axis=’columns’ (instead of axis=1).
The idiomatic way to do this with Pandas is to use the .sample method of your dataframe to sample all rows without replacement: The frac keyword argument specifies the fraction of rows to return in the random sample, so frac=1 means return all rows (in random order). Note: If you wish to shuffle your dataframe in-place and reset the index, you could … Read more
How about: To do all these operations in one-liner: Or
Try this: You are trying to get the values from a whole DataFrame. That is why you get the 2D array. Each row in the array is a row of the DataFrame.
You have four main options for converting types in pandas: to_numeric() – provides functionality to safely convert non-numeric types (e.g. strings) to a suitable numeric type. (See also to_datetime() and to_timedelta().) astype() – convert (almost) any type to (almost) any other type (even if it’s not necessarily sensible to do so). Also allows you to convert to categorial types (very useful). infer_objects() – … Read more
You can use the attribute df.empty to check whether it’s empty or not: Source: Pandas Documentation
It means: Source. The first character specifies the kind of data and the remaining characters specify the number of bytes per item, except for Unicode, where it is interpreted as the number of characters. The item size must correspond to an existing type, or an error will be raised. The supported kinds are to an … Read more
You should use sum: Then you use loc with Series, in that case the index should be set as the same as the specific column you need to sum: because if you pass scalar, the values of all rows will be filled: Two other solutions are with at, and ix see the applications below: Note: Since Pandas v0.20, ix has been deprecated. Use loc or iloc instead.
I usually create a dictionary containing a DataFrame for every sheet: Update: In pandas version 0.21.0+ you will get this behavior more cleanly by passing sheet_name=None to read_excel: In 0.20 and prior, this was sheetname rather than sheet_name (this is now deprecated in favor of the above):