I have two data frames where first dataframe has index starting from zero. The second dataframe has repeated indexes starting from zero. I want to to join the two dataframes based on their indexes. First dataframe is like this The second dataframe is I want to join these two dataframes based on index i.e the new dataframe should look like
Tag: data-science
Divide dataframe into list of rows containing all columns
From dataframe sructured like this I need to get list like this: Answer It looks like you want: example: output: other options
Python: How to filter a Pandas DataFrame using Values from a Series?
Context I am currently processing some data and encountered a problem. I would like to filter a Pandas DataFrame using Values from a Series. However, this always throws the following Error: ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all(). Code Question Does anyone have an idea what’s this error means and how
how to count data in a certain column in python(pandas)?
hope you’re doing well . i tried counting green color row after another green colored row in the table below In [1]: df = pd.DataFrame([[green], [red], [red]], columns=[‘A’]) the code i tried to count greengreen: but it didn’t work,hope you can help. note: i’m new to data science Answer You can use: As a one-liner (python ≥ 3.8): example input:
How to properly use Smote in Classification models
I am using smote to balanced the output (y) only for Model train but want to test the model with original data as it makes logic how we can test the model with smote created outputs. Please ask anything for clarification if I didn’t explained it well. It’s my starting on Stack overflow. Here i applied the Random Forest Classifier
How to count the same rows between multiple CSV files in Pandas?
I merged 3 different CSV(D1,D2,D3) Netflow datasets and created one big dataset(df), and applied KMeans clustering to this dataset. To merge them I did not use pd.concat because of memory error and solved with Linux terminal. All these datasets contain the same column names, they have 12 columns(all numerical values) Example expected result: cluster_0 has xxxx numbers of same rows
proper input and output shape of a keras Sequential model
I am trying to run a Keras sequential model but can’t get the right shape for the model to train on. I reshaped x and y to: Currently, both the input shape and output shape are: The dataset consists of 9766 inputs and 9766 outputs respectively. Each input is a single array of 500 values and each output is also
Got Nan while mapping the values in dataframe
Got NaN instead of man&woman What is wrong? Answer I think the type of gender is int, so this would fix your problem: The output:
How to add a new column rank on based on increasing value of other column in Pandas
I have this dataframe with which i am trying to create a new column rank on basis of increasing values of column Opportunity with pandas required output — Answer You can use rank function:
Operating large .csv file with pandas/dask Python
I’ve got a large .csv file (5GB) from UK land registry. I need to find all real estate that has been bought/sold two or more times. Each row of the table looks like this: I’ve never used pandas or any data science library. So far I’ve come up with this plan: Load the .csv file and add headers and column