I give you that this is a weird way of displaying the data, but the accuracy is the only field that don't fit the schema. For example:
precision recall f1-score support
0 0.84 0.97 0.90 160319
1 0.67 0.27 0.38 41010
As explained in How to interpret classification report of scikit-learn?, the precision, recall, f1-score and support are simply those metrics for both classes of your binary classification problem.
The second part of the table:
accuracy 0.82 201329 <--- WHAT?
macro avg 0.75 0.62 0.64 201329
weighted avg 0.80 0.82 0.79 201329
The accuracy is the overall accuracy of the model (note that accuracy is not a measure that is relative to a certain class, but a performance across all classes). The macro average for the precision and recall score is just the harmonic mean of the two classes. ie:
recall macro avg = (recall_class_1 + recall_class_0) / 2
The weighted average is just the average metric of the two classes weighted by the support/size-of-sample. ie:
recall weighted avg = (support_class_0 * recall_class_0 + support_class_1 * recall_class_1) / (support_class_0 + support_class_1)
This is a pretty long-winded way of saying the accuracy is just the overall accuracy. It has nothing to do with the column f1-score that it happens to be under.