Python ValueError: Invalid Literal for int(), Causes & Fixes (2026)
You ran your script, the user typed something, and Python slapped you with ValueError: invalid literal for int() with base 10. It’s one of the most common errors in Python, …
Python ValueError Reference: 100+ Library-Specific Fixes for pandas, NumPy, sklearn (2026)
itsourcecode.com hosts 100+ documented fixes for Python ValueError messages: the error class Python raises when a function or operation receives an argument of the right type but inappropriate value. Most ValueErrors come from data libraries: pandas with DataFrame indexing, NumPy with array shapes, scikit-learn with input dimensions, TensorFlow with tensor conversion, and matplotlib with axis coordinates. Browse by library or use the search bar to find your exact error.
What is a Python ValueError?
A ValueError is raised when a function receives an argument of the correct type but with an invalid value. For example, int("abc") raises ValueError because "abc" is a string (the right type for int()) but isn’t convertible to an integer. ValueError is distinct from TypeError (wrong type entirely) and from AttributeError (object doesn’t have that method). The data science stack (pandas, NumPy, scikit-learn) raises ValueErrors prolifically because they validate data shape and content extensively.
How to debug any ValueError in 4 steps
Read the full message. ValueError messages are usually descriptive, they tell you what value was invalid and what was expected (e.g., "could not convert string to float: 'N/A'", "expected 2d array, got 1d array instead").
Print the input that triggered the error. Add print(x, type(x), len(x) if hasattr(x, '__len__') else 'scalar') before the failing line. Most ValueErrors are about shape, length, or specific content.
Check the library’s expected input format. sklearn expects 2D arrays for features; pandas reindex expects unique labels; NumPy operations require compatible shapes. The docs for the failing function are usually clear about constraints.
For “could not convert” errors, validate the data first. Filter out NaN, infinity, empty strings, or invalid format values before passing to the function. Use pd.to_numeric(x, errors='coerce') for cleaning numeric columns.
Featured ValueError fixes by library
🐼 pandas (DataFrame, reindex, NaT, openpyxl)
pandas ValueErrors usually come from index conflicts, NaN handling, or version mismatches with Excel engines.
Cannot insert ‘level_0’, already exists, 2026 Guide
Cannot reindex on an axis with duplicate labels, 2026 Guide
DataFrame constructor not properly called
Unknown engine: openpyxl (Excel reader)
Cannot convert non-finite values (NA or inf) to integer
NaTType does not support strftime, 2026 Guide
No engine for filetype, 2026 Guide
Unconverted data remains, 2026 Guide
🔢 NumPy (shape, dimensional, argmax)
NumPy ValueErrors are usually array-shape problems, you’re trying to operate on arrays whose dimensions don’t match what the operation expects.
Object too deep for desired array, 2026 Guide
Need at least one array to concatenate, 2026 Guide
Attempt to get argmax of an empty sequence
Indices for endog and exog are not aligned (statsmodels)
Data must be 1-dimensional, 2026 Guide
🤖 scikit-learn (X and Y, classes, vocabulary)
sklearn ValueErrors are about input validation, wrong dimensions, wrong number of classes, or empty inputs.
X and Y must be the same size, 2026 Guide
Expected 2D array, got 1D array instead
Empty vocabulary; perhaps the documents only contain stop words
🧠 TensorFlow & Keras (cardinality, tensors)
Data cardinality is ambiguous
Can’t convert non-rectangular Python sequence to tensor
📊 matplotlib (axis, coordinates, sample size)
Coordinate ‘right’ is less than ‘left’
Sample larger than population or is negative, 2026 Guide
📷 PIL / Pillow (images)
Images do not match (same mode/size required)
🕐 datetime (strptime format mismatch)
The most common datetime ValueError is “unconverted data remains”, your format string doesn’t match the date string exactly.
Unconverted data remains, 2026 Guide
🐍 Python built-ins (int, float, unpack, regex)
Invalid literal for int() with base 10
Not enough values to unpack (expected 2, got 1)
Circular reference detected (json.dumps)
Pattern contains no capture groups (re.findall)
2026 Updated Guides: featured ValueError fixes
These guides were rewritten or expanded in 2026 with current library versions, minimal reproductions, and 3-4 alternative fixes each.
Empty vocabulary, perhaps the documents only contain stop words: sklearn CountVectorizer
Cannot insert ‘level_0’, already exists: pandas reset_index
Object too deep for desired array: NumPy
X and Y must be the same size: matplotlib / sklearn
Cannot reindex on an axis with duplicate labels: pandas
Sample larger than population or is negative: random.sample
Need at least one array to concatenate: np.concatenate
NaTType does not support strftime: pandas datetime
Data must be 1-dimensional: pandas Series
Unconverted data remains: datetime.strptime
No engine for filetype: pandas read_excel
Related error categories
ValueError is one of 10 hubs in our Python & JavaScript error reference cluster, 980+ documented fixes total. If your error isn’t a ValueError, jump to the right hub below:
TypeError Reference, 220+ Python & JS TypeError fixes
ModuleNotFoundError Reference, 198+ Python import errors
AttributeError Reference, 173+ “object has no attribute X” fixes
ImportError Reference, 67+ “cannot import name X from Y” fixes
NameError Reference, 49+ Python “name X is not defined” fixes
RuntimeError Reference, 49+ PyTorch CUDA, asyncio, Flask runtime errors
SyntaxError Reference, 48+ Python & JavaScript parsing errors
ReferenceError Reference, 34+ JavaScript “is not defined” fixes
HTTP Error Reference, 35+ HTTP status code fixes
Python Tutorial, beginner-to-intermediate Python lessons
About this ValueError reference
This ValueError reference has been built since 2015 by PIES Information Technology Solutions in Binalbagan, Negros Occidental, Philippines. Every post comes from a real error encountered in production code. Used by 12,000+ Python developers monthly across the Philippines, India, the United States, and beyond. If your ValueError isn’t here, send the full traceback to our contact form and we’ll add it.
[Fixed] ValueError: Empty Vocabulary Perhaps The Documents Only Contain Stop Words
In natural language processing (NLP) and text analytics, one of the common errors that researchers and practitioners encounter is the ValueError: Empty vocabulary perhaps the documents only contain stop words. …
[Fixed] ValueError: Cannot Insert Level_0 Already Exists — Python 2026
When you are working with data manipulation and analysis in Python, you may encounter an error message “ValueError: Cannot insert level_0, already exists“. This error is commonly encountered when trying …
[Fixed] ValueError: Object Too Deep For Desired Array — Python 2026
One of the common error that developer might have encounter is the ValueError: object too deep for desired array. This error usually occurs when we attempt to reshape or manipulate …
[Fixed] ValueError: X And Y Must Be The Same Size — Python 2026
In Python programming, encountering errors is not inevitable. One of the common errors is the ValueError: x and y Must Be the Same Size. This error typically occurs when working …
Valueerror: images do not match
In programming and computer vision, working with images is common work. However, sometimes you may encounter an error message that says “ValueError: Images do not match“. This error typically occurs …
Valueerror dataframe constructor not properly called
When working with DataFrames, it is not inevitable that you may encounter a common error known as “ValueError: DataFrame constructor not properly called“. This error typically occurs when the incorrect …
Valueerror: pattern contains no capture groups
In Python programming, developers often encounter different error messages while working on regular expressions. One of the error messages is the “ValueError: Pattern Contains No Capture Groups“. This error usually …
Valueerror: unknown engine: openpyxl
In programming and data analysis, encountering errors is not inevitable. One of the common errors that programmers often encounter is the ValueError: Unknown Engine: Openpyxl. This error typically occurs when …
unable to find resource t64.exe in package pip._vendor.distlib
One of the common error that programmer might encounter is valueerror: unable to find resource t64.exe in package pip._vendor.distlib. This error typically occurs when a Python package, especially pip._vendor.distlib, is …
Valueerror: coordinate ‘right’ is less than ‘left’
In programming, errors and bugs are an inevitable part of the development process. One of the common error that developers often encounter is the ValueError: coordinate ‘right’ is less than …
Valueerror: attempt to get argmax of an empty sequence
In Python programming, you might encounter the ValueError: attempt to get argmax of an empty sequence error when attempting to find the index of the maximum value in an empty …
Valueerror expected 2d array got 1d array instead
When you are working with arrays in programming, it is not uncommon to encounter errors that can prohibit the execution of your code. One of the errors that most of …
Frequently Asked Questions
What is the difference between ValueError and TypeError?
TypeError means the value's type is wrong for the operation entirely (None.split(), None cannot have any method called). ValueError means the type is correct but the specific value is invalid (int("abc"), string is OK input for int(), but "abc" is not convertible). If you are not sure: read the error message, TypeError messages mention object types in quotes, ValueError messages describe what value was invalid.
Why do pandas operations raise so many ValueErrors?
pandas validates DataFrame structure heavily, index uniqueness, column alignment, data types, and dimensions. When something does not match expectations, it raises ValueError early rather than producing silent bad results. The trade-off: more error noise during development, but you catch bugs immediately instead of debugging mysterious NaN-filled DataFrames later.
How do I fix "could not convert string to float: 'N/A'"?
Your data has non-numeric strings ('N/A', 'null', empty strings, etc.) in a column you are trying to convert to numeric. Two clean fixes: (1) Use pd.to_numeric(df['col'], errors='coerce'), this converts invalid strings to NaN, then you can drop or fill them. (2) Pre-clean the column with df['col'] = df['col'].replace(['N/A', 'null', ''], None) before conversion.
What does "expected 2D array, got 1D array instead" mean in sklearn?
sklearn estimators expect a 2D feature matrix (samples × features), even if you have only one feature. If X is a 1D array, reshape it: X.reshape(-1, 1) (one feature, many samples) or X.reshape(1, -1) (one sample, many features). Almost always you want the first form. This is the #1 ValueError beginners hit when starting sklearn.
How do I fix "unconverted data remains" with datetime?
Your format string does not capture the entire date string. Example: datetime.strptime("2026-01-15 10:30", "%Y-%m-%d") raises "unconverted data remains: ' 10:30'" because the format string stops at the date and ignores the time. Fix by extending the format: "%Y-%m-%d %H:%M", or trim the input string before parsing.
Should I catch ValueError with try/except?
Yes, when you are parsing untrusted input (user input, external API responses, CSV files with messy data). ValueError is the right exception to catch for "this input was malformed, skip it and continue." Do not catch it broadly in your own internal code, there it usually indicates a bug you should fix.
How often is this ValueError reference updated?
New posts are added weekly as we encounter errors in real projects. Existing posts are revised every 6-12 months when major library versions ship (pandas 2.x, sklearn 1.x, NumPy 2.x). This page was last refreshed in May 2026.