First argument must be an iterable of pandas objects [Fix]

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# First argument must be an iterable of pandas objects

The Pandas "TypeError: first argument must be an iterable of pandas objects, you passed an object of type "DataFrame" occurs when you pass a single DataFrame to the pandas.concat() method.

To solve the error, pass a list containing 2 DataFrames to the pandas.concat() method.

Here is an example of how the error occurs.

main.py

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import pandas as pd

df1 = pd.DataFrame({
    'letter': ['A', 'B'],
    'number': [1, 2]
})

df2 = pd.DataFrame({
    'letter': ['C', 'D'],
    'number': [3, 4]
})

df3 = pd.concat(df1)

# ⛔️ TypeError: first argument must be an iterable of pandas objects, you passed an object of type "DataFrame"
print(df3)

first argument must be iterable of pandas objects

Notice that we passed a single DataFrame to the pandas.concat() method.

# Pass a list containing 2 DataFrames to the `pandas.concat()` method

To solve the error, pass a list containing 2 DataFrames to the pandas.concat() method.

main.py

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import pandas as pd

df1 = pd.DataFrame({
    'letter': ['A', 'B'],
    'number': [1, 2]
})

df2 = pd.DataFrame({
    'letter': ['C', 'D'],
    'number': [3, 4]
})

df3 = pd.concat([df1, df2])

# 0      A       1
# 1      B       2
# 0      C       3
# 1      D       4
print(df3)

pass list containing 2 dataframes to pandas concat

The code for this article is available on GitHub

The pandas.concat() method takes a sequence of DataFrame objects.

Notice that we passed a list [] containing the two DataFrames to the method.

main.py

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# ✅ Correct
df3 = pd.concat([df1, df2])

Make sure you're not passing the DataFrames as multiple, individual arguments to the method.

main.py

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# ⛔️ Incorrect
df3 = pd.concat(df1, df2)

You can specify as many DataFrames as necessary in the list.

main.py

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# ✅ Correct
df4 = pd.concat([df1, df2, df3])

# Set the `ignore_index` argument to `True`

In most cases, you will want to set the ignore_index argument to True.

main.py

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import pandas as pd

df1 = pd.DataFrame({
    'letter': ['A', 'B'],
    'number': [1, 2]
})

df2 = pd.DataFrame({
    'letter': ['C', 'D'],
    'number': [3, 4]
})

df3 = pd.concat([df1, df2], ignore_index=True)

#   letter  number
# 0      A       1
# 1      B       2
# 2      C       3
# 3      D       4
print(df3)

set ignore index argument to true

The code for this article is available on GitHub

When the ignore_index argument is set to True, the index is reset in the resulting DataFrame.

Notice that the index goes 0, 1, 2, 3 instead of 0, 1, 0, 1.

# Pass a list of Series objects if concatenating Series

If you're trying to concatenate Series objects, pass them in a list to pandas.concat().

main.py

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import pandas as pd

s1 = pd.Series(['A', 'B'])
s2 = pd.Series(['C', 'D'])

s3 = pd.concat([s1, s2], ignore_index=True)

# 0    A
# 1    B
# 2    C
# 3    D
# dtype: object
print(s3)

pass list of series to pandas concat

The code for this article is available on GitHub

You can also specify the axis argument when calling pandas.concat().

When the axis is set to 1, the method will return a DataFrame.

main.py

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import pandas as pd

s1 = pd.Series(['A', 'B'])
s2 = pd.Series(['C', 'D'])

df = pd.concat([s1, s2], ignore_index=True, axis=1)

#    0  1
# 0  A  C
# 1  B  D
print(df)

setting the axis argument to 1

The code for this article is available on GitHub

The axis argument determines the axis to concatenate along.

By default, the argument is set to 0 (index).

Setting the axis argument to 1 means "concatenate along the columns axis".

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First argument must be an iterable of pandas objects [Fix]

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