I have a pandas DataFrame with 4 columns and I want to create a new DataFrame that only has three of the columns. This question is similar to: Extracting specific columns from a data frame but for pandas not R. The following code does not work, raises an error, and is certainly not the pandasnic way to do it.
this is a rather similar question to this question but with one key difference: I’m selecting the data I want to change not by its index but by some criteria.
If I have the following dataframe, derived like so: df = pd.DataFrame(np.random.randint(0, 10, size=(10, 1)))
I am trying to implement an LSTM with Keras.
I try to override globally defined Dataframes from within a function.
Somehow the global values do not change, printing the dataframes within the functions works with the expected values.
There are 2 different ways how these widgets can access their data. The traditional way involves widgets which include internal containers for storing data. This approach is very intuitive, however, in many non-trivial applications, it leads to data synchronization issues. The second approach is model/view programming, in which widgets do not maintain internal data containers
I am trying to create a column (“consec”) which will keep a running count of consecutive values in another (“binary”) without using loop. This is what the desired outcome would look like:
I have a pandas data frame. One of the columns contains a list. I want that column to be a single string.
I have two DataFrames . . .
I’ve got pandas data with some columns of text type. There are some NaN values along with these text columns. What I’m trying to do is to impute those NaN’s by sklearn.preprocessing.Imputer (replacing NaN by the most frequent value). The problem is in implementation.
Suppose there is a Pandas dataframe df with 30 columns, 10 of which are of categorical nature.
Once I run: