Dataframe constructor not properly called ошибка

Pandas module allows you to create and manipulate dataframe. You can create a pandas dataframe using the pandas.DataFrame() constructor. But many beginners may face errors like ValueError: DataFrame constructor not properly called! And it can be due to many factors. In this entire post, you will learn how to solve the issue of dataframe constructor not properly called error.

The main reason for getting this error is that you must be using the DataFrame() constructor in the wrong way. For example, you must be passing the data to the constructor of an invalid type.

Suppose I will pass the data of type string then I will get the constructor not properly called error.

import pandas as pd
data = "Data Science Learner" #string value
df = pd.DataFrame(data)
print(df)

Output

In the same way, If I will pass the integer or float will get the same error.

import pandas as pd
data = 1000 # integer value
df = pd.DataFrame(data)
print(df)

Output

import pandas as pd
data = 10.11 # float value
df = pd.DataFrame(data)
print(df)

Output

Solution for the dataframe constructor not properly called error

The solution for this type of error is that you have to pass the data to constructor pandas.DataFrame() in a  proper way. For example you will pass the data to the constructor in the form key-value pairs like below. The number of elements for each of the column name should be the same otherwise it will lead to an error.

import pandas as pd
data = {"col1":[1,2,3],"col2":[4,5,6]}
df = pd.DataFrame(data)
print(df)

Output

The other solution is to add the data to the pandas.DataFrame() as the list of lists. In this case, each list will act as a record or row of the dataframe.

You will not get the error when you will execute the below lines of code.

import pandas as pd
data = [["Robin",10],["Maya",20],["George",30]]
df = pd.DataFrame(data)
print(df)

Output

Conclusion

If you are getting the error ValueError constructor not properly called! then the obvious case for getting is that you are not properly using the pandas.DataFrame() constructor. The above way of creating dataframe will solve this error.

I hope you have liked this tutorial and solved your query. In case you have any doubts then you can contact us for more help.

Pandas dataframes are used to manipulate tabular data in python. While data manipulation, we sometimes need to convert data from other python objects such as lists, strings, and tuples into a dataframe. During conversion, you might get into an exception with the message ValueError: DataFrame constructor not properly called! This article discusses the ValueError: DataFrame constructor not properly called! error, its causes, and the solutions.

What Is ValueError: Dataframe Constructor Not Properly Called! Error in Python?

As the message suggests the error “ValueError: DataFrame constructor not properly called! ” is a python ValueError exception. It means that the error occurs when we pass an incompatible value as input to the DataFrame() function. This may happen in the following cases.

1. We pass a string as the input to the DataFrame() function.
2. When we pass a string representation of a list instead of a list to the DataFrame() function.
3. We pass a JSON string directly to the DataFrame() function. 
4. When we pass a string representation of a python dictionary instead of a dictionary to the DataFrame() function.
5. We pass other scalar values such as integers or floating point numbers to the  DataFrame() function. 

When Does the ValueError: DataFrame Constructor Not Properly Called Error Occur?

As introduced above the exception occurs in five cases. Let us discuss each of them one by one.

When We Pass a String as the Input to the DataFrame() Function

In most instances,  ValueError: DataFrame constructor not properly called error occurs when we try to create an empty dataframe with a given column name. When we pass the column name directly to the DataFrame() function, the program runs into the ValueError exception. You can observe this in the following example.

import pandas as pd
columnName="Column1"
print("The column name is:")
print(columnName)
df=pd.DataFrame(columnName)
print("The dataframe is:")
print(df)

Output:

The column name is:
Column1
ValueError: DataFrame constructor not properly called!

In this example, we tried to create an empty dataframe with the column name Column1. As we passed the column name directly to the DataFrame() function, the program runs into ValueError exception.

We Pass a String to the DataFrame() Function

We often create a dataframe from a list in python. You might think that you can also create a dataframe of characters in the string by passing the string to the DataFrame() function as an input argument. However, the program runs into the ValueError: DataFrame constructor not properly called!  Exception.

You can observe this in the following example.

import pandas as pd
myStr="PFB"
print("The string is:")
print(myStr)
df=pd.DataFrame(myStr)
print("The dataframe is:")
print(df)

Output:

The string is:
PFB
ValueError: DataFrame constructor not properly called!

In this example, we passed the string "PFB" to the DataFrame() function to create a dataframe. Due to this, the program runs into ValueError Exception.

When we pass a string representation of a list to the DataFrame() function, the program runs into ValueError: DataFrame constructor not properly called exception as shown below.

import pandas as pd
listStr='[1,22,333,4444,55555]'
print("The list string is:")
print(listStr)
df=pd.DataFrame(listStr)
print("The dataframe is:")
print(df)

Output:

The list string is:
[1,22,333,4444,55555]
ValueError: DataFrame constructor not properly called!

In the above example, we passed a string "[1,22,333,4444,55555]" to the DataFrame() function. Due to this, the program runs into ValueError exception.

In a similar manner, when we pass a string representation of a dictionary to the DataFrame() function, the program runs into a ValueError exception as shown in the following example.

import pandas as pd
dictStr='{"Roll":1,"Maths":100, "Physics":80, "Chemistry": 90}'
print("The dictionary string is:")
print(dictStr)
df=pd.DataFrame(dictStr)
print("The dataframe is:")
print(df)

Output:

The dictionary string is:
{"Roll":1,"Maths":100, "Physics":80, "Chemistry": 90}
ValueError: DataFrame constructor not properly called!

In some cases, we might also directly try to convert a JSON string into a pandas dataframe using the DataFrame() function. In these cases, the program will run into errors as shown below.

import pandas as pd
jsonStr='{"Roll":1,"Maths":100, "Physics":80, "Chemistry": 90}'
print("The json string is:")
print(jsonStr)
df=pd.DataFrame(jsonStr)
print("The dataframe is:")
print(df)

Output:

The json string is:
{"Roll":1,"Maths":100, "Physics":80, "Chemistry": 90}
ValueError: DataFrame constructor not properly called!

When We Pass a Scalar Value to the DataFrame() Function

We can create a dataframe from an iterable object such as a list, tuple, set, or dictionary. However, when we pass an object of primitive data types such as integer or floating point number as input to the DataFrame() function, the program runs into the ValueError exception with the message ValueError: DataFrame constructor not properly called!.

You can observe this in the following example.

import pandas as pd
myInt=1117
print("The integer is:")
print(myInt)
df=pd.DataFrame(myInt)
print("The dataframe is:")
print(df)

Output:

The integer is:
1117
ValueError: DataFrame constructor not properly called!

In this example, we passed the integer 1117 to the DataFrame() function. Due to this, the program runs into ValueError exception.

How to Solve ValueError: DataFrame Constructor Not Properly Called Exception in Python?

In accordance with the reasons of the error, we can solve the ValueError: DataFrame constructor not properly called exception using various ways.

Use the columns Parameter to Assign Column Names to the Dataframe

The first way to solve the  ValueError: DataFrame constructor not properly called exception in Python is to not pass a string directly to the DataFrame() constructor. If you are trying to create a dataframe with a given column name as a string, use the columns parameter in the constructor as shown below.

import pandas as pd
columnName="Column1"
print("The column name is:")
print(columnName)
df=pd.DataFrame(columns=[columnName])
print("The dataframe is:")
print(df)

Output:

The column name is:
Column1
The dataframe is:
Empty DataFrame
Columns: [Column1]
Index: []

In this example, we passed the string "Column1" to the columns parameter after putting it in a list. Due to this, the program executes successfully and we get an empty dataframe with the given column name.

Pass a List of Strings as Input to the DataFrame() Function

If you want to create a dataframe from the characters of the list. You can first convert the string to a list of characters. Then, you can pass the list as input to the DataFrame() constructor to avoid the  ValueError: DataFrame constructor not properly called exception in Python. You can observe this in the following example.

import pandas as pd
myStr="PFB"
print("The string is:")
print(myStr)
df=pd.DataFrame(list(myStr))
print("The dataframe is:")
print(df)

Output:

The string is:
PFB
The dataframe is:
   0
0  P
1  F
2  B

In this example, we first created a list of characters using the string "PFB" and the list() function. Then, we passed the list of characters to the the DataFrame() function to create the output dataframe.

If you want to put the string as an element of the data frame, you can put the string in a list and then pass the list to the DataFrame() function as shown below.

import pandas as pd
myStr="PFB"
print("The string is:")
print(myStr)
df=pd.DataFrame([myStr])
print("The dataframe is:")
print(df)

Output:

The string is:
PFB
The dataframe is:
     0
0  PFB

Convert Strings Into Python Objects Before Passing Them to the DataFrame() Function

If you want to convert a JSON string to a dataframe, first convert the json string to a python dictionary. Then, you can pass the dictionary to the DataFrame() function as shown below.

import pandas as pd
import json
jsonStr='{"Roll":1,"Maths":100, "Physics":80, "Chemistry": 90}'
print("The json string is:")
print(jsonStr)
myDict=json.loads(jsonStr)
df=pd.DataFrame([myDict])
print("The dataframe is:")
print(df)

Output:

The json string is:
{"Roll":1,"Maths":100, "Physics":80, "Chemistry": 90}
The dataframe is:
   Roll  Maths  Physics  Chemistry
0     1    100       80         90

If you have a string representation of a list or dictionary and you want to convert it into a dataframe, first convert the string into a list or dictionary. For this, you can use the eval() method. The eval() method takes the string representation of the list or dictionary and converts them into a python list or dictionary respectively. After this, you can use the list to create a dataframe as shown below.

import pandas as pd
dictStr='{"Roll":1,"Maths":100, "Physics":80, "Chemistry": 90}'
print("The dictionary string is:")
print(dictStr)
myDict=eval(dictStr)
df=pd.DataFrame([myDict])
print("The dataframe is:")
print(df)

Output:

The dictionary string is:
{"Roll":1,"Maths":100, "Physics":80, "Chemistry": 90}
The dataframe is:
   Roll  Maths  Physics  Chemistry
0     1    100       80         90

Conclusion

In this article, we discussed the  ValueError: DataFrame constructor not properly called exception in Python. We also discussed the possible cause and solutions for this error. To learn more about python programming, you can read this article on how to overwrite a file in python. You might also like this article on CPython vs Python.

I hope you enjoyed reading this article. Stay tuned for more informative articles!

Happy Learning!