ValueError: If using all scalar values, you must pass index

# Table of Contents

1. ValueError: If using all scalar values, you must pass index
2. Wrap each value in a list [] to solve the error
3. Creating a DataFrame that contains scalar values with index
4. Wrap the dictionary in a list to solve the error
5. Solving the error by using Series.to_frame()
6. Maybe you meant to use the Series class
7. Specifying the columns explicitly to solve the error
8. Setting the orient to "index" to solve the error

# ValueError: If using all scalar values, you must pass index

The Pandas "ValueError: If using all scalar values, you must pass an index" occurs when you try to create a DataFrame containing only scalar values (e.g. integers, strings or floats).

To solve the error, wrap each value in a list [] or set the index argument when creating the DataFrame.

Here is an example of how the error occurs.

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import pandas as pd

df = pd.DataFrame({
    'A': 50,
    'B': 100,
    'C': 150
})

# ⛔️ ValueError: If using all scalar values, you must pass an index
print(df)

We instantiated the DataFrame class with only scalar values (integers) without supplying the index argument.

# Wrap each value in a list [] to solve the error

One way to solve the error is to wrap each value in a list by using square brackets.

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import pandas as pd

df = pd.DataFrame({
    'A': [50],
    'B': [100],
    'C': [150]
})

print(df)

Running the code sample produces the following output.

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    A    B    C
0  50  100  150

Notice that we wrapped the DataFrame values in square brackets [] to use lists as values instead of scalars (e.g. integers, strings or floats).

When using this approach, you can specify multiple rows by separating the list items with a comma.

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import pandas as pd

df = pd.DataFrame({
    'A': [50, 60, 70],
    'B': [100, 110, 1120],
    'C': [150, 160, 170]
})

print(df)

print('-' * 50)

print(df['A'])

print('-' * 50)

print(df['B'])

# Creating a DataFrame that contains scalar values with index

If you want to create a DataFrame that only contains scalar values, you have to supply the index argument.

Copied!
import pandas as pd

df = pd.DataFrame({
    'A': 50,
    'B': 100,
    'C': 150
},
    index=[0]
)

#     A    B    C
# 0  50  100  150
print(df)

print('-' * 50)

# 0    50
# Name: A, dtype: int64
print(df['A'])

The index argument is used to specify the index to use for the resulting DataFrame.

When you construct a DataFrame containing only scalar values, you should only pass a single value in the index list.

# Wrap the dictionary in a list to solve the error

You can also solve the error by wrapping the dictionary in a list [].

Copied!
import pandas as pd

a_dict = {
    'A': 50,
    'B': 100,
    'C': 150
}

df = pd.DataFrame([a_dict])

#     A    B    C
# 0  50  100  150
print(df)

Notice that we wrapped the dictionary in square brackets [].

This way, we passed a list containing a dictionary to the pandas.DataFrame() class (and not a dictionary with scalar values).

Now the index is implied to be 0.

We could've also used the DataFrame.from_records() method.

Copied!
import pandas as pd

a_dict = {
    'A': 50,
    'B': 100,
    'C': 150
}

df = pd.DataFrame.from_records([a_dict])

#     A    B    C
# 0  50  100  150
print(df)

The DataFrame.from_records() method converts a structured or record ndarray to a DataFrame.

You don't have to explicitly set the index when calling from_records() but you could.

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import pandas as pd

a_dict = {
    'A': 50,
    'B': 100,
    'C': 150
}

df = pd.DataFrame.from_records([a_dict], index=[0])

#     A    B    C
# 0  50  100  150
print(df)

# Solving the error by using Series.to_frame()

You can also use the Series.to_frame method to solve the error.

Copied!
import pandas as pd

a_dict = {
    'A': 50,
    'B': 100,
    'C': 150
}

df = pd.Series(a_dict).to_frame()

#      0
# A   50
# B  100
# C  150
print(df)

The pandas.Series is used to create a one-dimensional ndarray with axis labels.

Series objects have a to_frame() method that converts the Series to a DataFrame.

The method returns the DataFrame representation of the series.

# Maybe you meant to use the Series class

You might've also meant to use the pandas.Series class instead.

Copied!
import pandas as pd

a_dict = {
    'A': 50,
    'B': 100,
    'C': 150
}

series = pd.Series(a_dict)

# A     50
# B    100
# C    150
# dtype: int64
print(series)

print('-' * 50)

df = pd.DataFrame()

df = df.assign(col_name=series.values)

#    col_name
# 0        50
# 1       100
# 2       150
print(df)

The code sample uses the Series class to create a Series object and then adds the values of the Series to a DataFrame column.

# Specifying the columns explicitly to solve the error

You can also solve the error by specifying the columns explicitly when instantiating the DataFrame() class.

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import pandas as pd

a_dict = {
    'A': 50,
    'B': 100,
    'C': 150
}

df = pd.DataFrame(a_dict.items(), columns=['X', 'Y'])

#    X    Y
# 0  A   50
# 1  B  100
# 2  C  150
print(df)

print('-' * 50)

# 0    A
# 1    B
# 2    C
# Name: X, dtype: object
print(df['X'])

The columns argument is used to set the columns labels to use for the resulting DataFrame when the supplied data doesn't have any.

# Setting the orient to "index" to solve the error

You can also solve the error by setting the orient argument to "index" when calling DataFrame.from_dict.

Copied!
import pandas as pd

a_dict = {
    'A': 50,
    'B': 100,
    'C': 150
}

df = pd.DataFrame.from_dict(a_dict, orient='index')

#      0
# A   50
# B  100
# C  150
print(df)

The DataFrame.from_dict() method constructs a DataFrame from a dictionary of array-like objects or nested dictionaries.

The orient argument is used to specify the "orientation" of the data.

When the orient argument is set to "index", the keys are used as rows.

# Additional Resources

You can learn more about the related topics by checking out the following tutorials:

• Multiply the Values in a Dictionary in Python
• Calculate the Average for each Row in a Pandas DataFrame
• How to drop all Rows in a Pandas DataFrame in Python
• How to drop all Rows in a Pandas DataFrame in Python
• Pandas: Get Nth row or every Nth row in a DataFrame
• Pandas: Select Rows between two values in DataFrame