pandas DataFrames
The to_csv method, called on a pandas DataFrame, saves that DataFrame to a CSV file named by its first argument.
Three Splits, Three Files
Before a model can be trained and checked, a dataset is usually divided into separate parts: a training split, a validation split, and a test split. Each part lives in memory as a pandas DataFrame while your program runs. The final step of that preparation stage is writing those DataFrames out to CSV files, so the splits are stored on disk where other programs and later sessions can read them.
The source for this section is only three lines long, one line for each split. Before reading the explanation, trace those three lines yourself. For each line, ask which file it produces and what the filename tells you about its destination. Keep a note of any question you have about the result, because the rest of this article is built around checking those results.
Following the Three Save Calls
Trace the three save calls from the source. Each DataFrame has its own call to to_csv, and each call writes to its own file. Nothing is combined. The training data goes to train.csv, the validation data goes to validation.csv, and the test data goes to test.csv. Follow each arrow from the DataFrame on the left to the file on the right and check that the names match.
Notice that the diagram has no crossing lines. Each DataFrame has exactly one outgoing save and each file receives exactly one DataFrame. That one-to-one pattern is the thing to check. If any arrow in your own code points from a DataFrame to a file whose name does not describe that DataFrame, you have found a mismatch to investigate.
What to_csv Does to a DataFrame
The to_csv method, called on a pandas DataFrame, saves that DataFrame to a CSV file named by its first argument.
A DataFrame is the object that holds the data while your program runs. A CSV file is a plain text file on disk. The to_csv method is the bridge between the two. The DataFrame stays in memory, and the method produces a file that holds the same data in a form that other programs and later sessions can read. Because the method is called on the DataFrame itself, the name of the DataFrame variable appears before the dot.
A saved CSV file is a snapshot. It records the DataFrame exactly as it was at the moment to_csv ran. The file does not follow later changes to the DataFrame in memory. If you change the DataFrame afterwards, the file stays the same until you call to_csv again.
What do you think happens?
You save train_df to train.csv, and then you add a new row to train_df in memory. Without saving again, what happens to train.csv?
Reveal answer
It stays the same, holding the contents from the moment of the save
A saved CSV is a snapshot taken when to_csv runs. Changes to the DataFrame in memory do not reach the file until to_csv is called again.
Reading a to_csv Call
Every save line in the source follows the same template. Read each line from left to right. The DataFrame name tells you what is being saved, the method name tells you what happens to it, the string tells you where it goes, and the index argument tells you how the save is configured. Reading the line in this order is the main skill this section asks for, because a wrong name in any one position produces a file that does not match what you intended.
Tracing a Single Save Step by Step
The following example uses a small generated DataFrame, not the real dataset, to show how to trace a single save. The goal is to follow each step in order instead of guessing the result.
- Line 3 builds a small
DataFramein memory with two rows. - Line 4 calls
to_csvontrain_df, sotrain_dfis theDataFramebeing saved. - The string
train.csvis the filename and says the file is written totrain.csv. - index=False is the index argument for this call.
Tracing one save line
Given the save line train_df.to_csv('train.csv', index=False), identify the DataFrame, the file, and the index setting, and state what the file should contain.
- 1Identify the DataFrame
The name before the dot is
train_df, so the training split is what gets saved. - 2Identify the filename
The first argument is '
train.csv', so the output is a file namedtrain.csv. - 3Identify the index setting
The index argument is False, which is the setting this call uses for the row labels.
- 4Check the match
The
DataFrameis training data and the filename says train, so the name and the data agree.
The file train.csv receives a snapshot of the training DataFrame as it is at the moment of the call.
Mistakes When Saving Splits
Most errors in this step come from a single wrong token in a save line. Each mistake below is one that produces a file that does not match what you intended.
Common mistake Passing the wrong DataFrame variable name
What it looks liketest_df.to_csv('train.csv', index=False) when the intent was to save the training split
Why it failsThe
DataFramenamed before the dot is the data that gets written, so the file receives test rows under a training name.Do this insteadRead the name before the dot first and confirm it matches the filename's purpose.
Common mistake Using the wrong filename for a DataFrame
What it looks likevalidation_df.to_csv('test.csv', index=False)Why it failsThe filename is the destination, so the validation data lands in the file meant for test data.
Do this insteadCheck that each DataFrame's name and its filename describe the same split.
Common mistake Reusing one filename for two different splits
What it looks likeSaving validation_df to 'train.csv' after train_df was already saved there
Why it failsThe filename decides the destination, so the later save writes to the same file, and the training data that was saved there is no longer the file's content.
Do this insteadGive every split its own filename and scan the save lines for duplicates.
Common mistake Expecting a saved file to track later changes
What it looks likeModifying train_df after saving it and assuming train.csv was updated
Why it failsA saved CSV is a snapshot taken at the moment
to_csvruns.Do this insteadCall to_csv again after any change you want reflected in the file.
Tracing Your Own Save Calls
A program saves three DataFrames with these lines: train_df.to_csv('train.csv', index=False), validation_df.to_csv('test.csv', index=False), and test_df.to_csv('test.csv', index=False). Which DataFrame ends up in each file, and which save line should you examine first?
Hints
- Apply the four questions to each line separately: which
DataFrame, which filename, which index setting, and what it held at the call. - Look for two lines that name the same file.
- Compare the
DataFramename on the second line with the filename it writes to.
Key Takeaways
- The
to_csvmethod, called on a pandasDataFrame, saves thatDataFrameto a CSV file named by its first argument. - A saved CSV is a snapshot of the
DataFrameat the momentto_csvruns; later changes in memory need another save. - Each save line should be read left to right: the
DataFrame, the method, the filename, and the index argument. - Each split should be saved to its own file, and a wrong
DataFramename or filename produces a mismatched file that can be traced line by line.