Convert Pandas dataframe to PyTorch tensor?

Question

I want to train a simple neural network with PyTorch on a pandas dataframe df.

One of the columns is named "Target", and it is the target variable of the network. How can I use this dataframe as input to the PyTorch network?

I tried this, but it doesn't work:

import pandas as pd import torch.utils.data as data_utils  target = pd.DataFrame(df['Target']) train = data_utils.TensorDataset(df, target) train_loader = data_utils.DataLoader(train, batch_size=10, shuffle=True) 

Answer 1

I'm referring to the question in the title as you haven't really specified anything else in the text, so just converting the DataFrame into a PyTorch tensor.

Without information about your data, I'm just taking float values as example targets here.

Convert Pandas dataframe to PyTorch tensor?

import pandas as pd import torch import random  # creating dummy targets (float values) targets_data = [random.random() for i in range(10)]  # creating DataFrame from targets_data targets_df = pd.DataFrame(data=targets_data) targets_df.columns = ['targets']  # creating tensor from targets_df  torch_tensor = torch.tensor(targets_df['targets'].values)  # printing out result print(torch_tensor) 

Output:

tensor([ 0.5827,  0.5881,  0.1543,  0.6815,  0.9400,  0.8683,  0.4289,          0.5940,  0.6438,  0.7514], dtype=torch.float64) 

Tested with Pytorch 0.4.0.

I hope this helps, if you have any further questions - just ask. :)