df.iloc[i] returns the ith row of df. i does not refer to the index label, i is a 0-based index. In contrast, the attribute index returns actual index labels, not numeric row-indices: or equivalently, You can see the difference quite clearly by playing with a DataFrame with a non-default index that does not equal to the row’s numerical position: If you want to use the index, … Read more
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
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.
What if you do this (as was suggested earlier):
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
One way to convert to string is to use astype: However, perhaps you are looking for the to_json function, which will convert keys to valid json (and therefore your keys to strings): Note: you can pass in a buffer/file to save this to, along with some other options…
You can use the attribute df.empty to check whether it’s empty or not: Source: Pandas Documentation
I believe this is what you want: Example: