I know there are many questions like this one but I can’t seem to find the relevant answer. Let’s say I have 2 data frames as follow: Resulted as: The classic way to merge by ID so timestamp will be between start and end in df1 is by merge on id or dummy variable and filter: In which I get
Tag: dataframe
How to know a movie has how many 0.5/1/1.5/2/2.5/3/3.5/4/4.5/5 rating that rated by every user?
I would like to know how many 0.5/1/1.5/2/2.5/3/3.5/4/4.5/5 ratings that rated by every user in a data frame of a certain movie which is Ocean’s Eleven (2001) in order to calculate Pearson Correlation using the formula. Below is the code In this case, I just can know there are two 4.0 ratings, but I have around 600+ users. Because I
Create rolling average pandas
I have a dataset of esports data like this: (done using pd.to_clipboard() I want to create a dataframe that essentially, for each team, every week, creates a rolling X game average of their points scored. (X could be 2, 3, 4, etc). A few notes: This example only shows points, the actual data has about 10 features that need rolling
Replacing NONE with Nan – but it Reappears in Subsequent Output of Code
I am trying to replace None (not recognized as a string) with nan — and fill those nans with the mode of the field, but when I further condense the field — None appears back in the output. What am I missing? None is back… What am I missing/doing wrong here? If I rerun that last section, None will disappear
Change all values in column if a condition is met within a group in Pandas dataframe
I have a dataframe that contains many rows, and a condition that is checked for each row and saved as a boolean in a column named condition. If this condition is False for any row within a group, I want to create a new column that is set to False for the whole group, and to True if the condition
How to efficiently combine multiple pandas columns into one array-like column?
It is easy to create (or load) a DataFrame with something like an object-typed column, as so: I am currently in the position where I have, as separate columns, values that I am required to return as a single column, and need to do so quite efficiently. Is there a fast and efficient way to combine columns into a single
Merging 2 different DataFrame with different length
I have two DataFrame Consists of time and price columns. I want to create a new DataFrame df3 as the length of df2, and I also want to put df1[‘price’] in it like below Where price1 shows the mean of price1 values for the corresponding time2 values like below I’m sorry if it’s unclear, but could you advise me on
seaborn: ‘rows’ and ‘x_vars’ at the same time
I want a seaborn multiplot that varies the x-axis variable by column, but varies the subset of data shown by row. I can use PairGrid to vary the variables graphed, and I can use FacetGrid to vary the subsets graphed, but I don’t see any facility to do both at once, even though it seems like a natural extension. Is
Cleaner way to selectively multiply pandas DataFrame values
Given this example: Where the values in df are multiplied by non-NaN values from factors, is there a cleaner way to do this with pandas? (or numpy for that matter) I had a look at .mul(), but that doesn’t appear to allow me to do what’s required here. Additionally, what if factors contains rows with an id that’s not in
Pandas “A value is trying to be set on a copy of a slice from a DataFrame”
Having a bit of trouble understanding the documentation See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy dfbreed[‘x’] = dfbreed.apply(testbreed, axis=1) C:/Users/erasmuss/PycharmProjects/Sarah/farmdata.py:38: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead Code is basically to re-arrange and clean some data to make analysis easier. Code in given row-by