how-do-i-select-rows-from-a-dataframe-based-on-column-values
To select rows whose column value equals a scalar, some_value, use ==:
df.loc[df['column_name'] == some_value]To select rows whose column value is in an iterable, some_values, use isin:
df.loc[df['column_name'].isin(some_values)]how-do-i-sort-a-dictionary-by-value
x = {1: 2, 3: 4, 4: 3, 2: 1, 0: 0}
dict(sorted(x.items(), key=lambda item: item[1]))how-can-i-count-the-occurrences-of-a-list-item
from collections import Counter
l = ["a","b","b"]
Counter(l)pandas.DataFrame.drop_duplicates
df = pd.DataFrame({... 'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
... 'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
... 'rating': [4, 4, 3.5, 15, 5]
... })
df.drop_duplicates(subset=['brand'])tf.data.Dataset—–as_numpy_iterator()
Returns an iterator which converts all elements of the dataset to numpy.
dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3])
for element in dataset.as_numpy_iterator():
print(element)tf.data.Dataset
The tf.data.Dataset API supports writing descriptive and efficient input pipelines. Dataset usage follows a common pattern:
- Create a source dataset from your input data.
- Apply dataset transformations to preprocess the data.
- Iterate over the dataset and process the elements.
Iteration happens in a streaming fashion, so the full dataset does not need to fit into memory.
The simplest way to create a dataset is to create it from a python list:
dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3])
for element in dataset:
print(element)Once you have a dataset, you can apply transformations to prepare the data for your model:
dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3])
dataset = dataset.map(lambda x: x*2)
list(dataset.as_numpy_iterator())