For a numeric DataFrame whose columns share a compatible dtype, use tf.convert_to_tensor(df). If the columns have different kinds of values, prepare them deliberately or keep them as separate named inputs; one TensorFlow tensor cannot hold elements with different dtypes.
Convert a compatible DataFrame directly
When the selected columns are homogeneous and already use a representation your model or operation accepts, pass the DataFrame to TensorFlow:
import tensorflow as tf
x = tf.convert_to_tensor(df)TensorFlow’s Load a pandas DataFrame tutorial explains that a uniform-dtype DataFrame can be used anywhere a NumPy array can be used. The TensorFlow conversion API infers the dtype when you omit it. Check the resulting tensor if a particular dtype or shape is required downstream.
Make the NumPy conversion and dtype explicit
Use DataFrame.to_numpy() when you want to make the array conversion visible or specify the representation before handing values to TensorFlow:
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x = tf.convert_to_tensor(df.to_numpy(dtype="float32"))
# Alternatively, specify the TensorFlow dtype:
x = tf.convert_to_tensor(df.to_numpy(), dtype=tf.float32)Pandas documents DataFrame.to_numpy() as returning an ndarray. Choosing float32 is a conversion decision, not merely a formatting step: confirm that the values can be represented appropriately and that the model expects that dtype.
Handle mixed-type columns without forcing one tensor
A tensor has one element dtype. If a frame mixes numeric, text, categorical, or other feature types, pandas may coerce columns to a common NumPy dtype; some combinations produce an object array, which is generally not a useful numeric tensor input. Inspect the column dtypes and the extracted array before converting:
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print(df.dtypes)
print(df.to_numpy().dtype)For heterogeneous features, keep columns named and separate in a dictionary, then create a dataset from the dictionary:
feature_columns = {
name: series.to_numpy()[:, None]
for name, series in df.items()
}
dataset = tf.data.Dataset.from_tensor_slices(feature_columns)This follows the structure shown in TensorFlow’s DataFrame tutorial: each feature remains its own input, and [:, None] adds a singleton feature axis. Adapt the column preprocessing, shapes, batching, and labels to what your model expects. Text, categories, and datetimes need an intentional model-compatible encoding; blindly casting them to numbers does not give them meaningful numeric semantics.
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Choose the conversion path
| Path | Use it when | Trade-off |
|---|---|---|
tf.convert_to_tensor(df) |
The selected DataFrame is homogeneous and model-ready. | Concise; TensorFlow infers the dtype, so inspect it if dtype matters. |
tf.convert_to_tensor(df.to_numpy(dtype="float32")) |
You want explicit array extraction and a chosen dtype. | Casting must be valid for your values; conversion can coerce or copy data. |
| Dictionary of column arrays | Features have different dtypes or should remain separate by name. | Preserves per-column structure, but the model’s input pipeline must handle or transform each feature. |
Check missing values, memory, and shape
- Missing values: Decide how to fill, impute, or otherwise represent missing data before conversion. Pandas’
to_numpy()has anna_valueoption, and its default depends on the column dtypes; the right policy depends on the dataset and model. - Memory use: Do not assume
to_numpy(copy=False)avoids allocation. Pandas notes thatcopy=Falsedoes not guarantee a no-copy view; mixed dtypes, coercion, or extension-backed columns can require a copy. See the pandas API reference. - Shape: A DataFrame commonly represents rows by columns, producing a two-dimensional feature matrix. A model may instead expect separate feature tensors or a different rank. TensorFlow’s tutorial examples show column arrays with an added singleton axis for a dataset input pipeline.
Use a DataFrame with Keras when the inputs are ready
TensorFlow’s tutorial also demonstrates supplying a homogeneous DataFrame to Model.fit, including a numeric-feature example that adapts a Keras normalization layer before training. That is a supported example for appropriately prepared input, not a guarantee that any DataFrame will work unchanged with every model. Match feature preprocessing, labels, dtype, and shape to the model’s input contract.
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