Python | Панды dataframe.infer_objects ()
Python - отличный язык для анализа данных, в первую очередь из-за фантастической экосистемы пакетов Python, ориентированных на данные. Pandas - один из таких пакетов, который значительно упрощает импорт и анализ данных.
Pandas dataframe.infer_objects() function attempts to infer better data type for input object column. This function attempts soft conversion of object-dtyped columns, leaving non-object and unconvertible columns unchanged. The inference rules are the same as during normal Series/DataFrame construction.
Syntax: DataFrame.infer_objects()
Returns : converted : same type as input object
Example #1: Use infer_objects() function to infer better data type.
# importing pandas as pdimport pandas as pd # Creating the dataframe df = pd.DataFrame({"A":["sofia", 5, 8, 11, 100], "B":[2, 8, 77, 4, 11], "C":["amy", 11, 4, 6, 9]}) # Print the dataframedf |
Выход :
Let’s see the dtype (data type) of each column in the dataframe.
# to print the basic infodf.info() |
As we can see in the output, first and third column is of object type. whereas the second column is of int64 type. Now slice the dataframe and create a new dataframe from it.
# slice from the 1st row till enddf_new = df[1:] # Let"s print the new data framedf_new # Now let"s print the data type of the columnsdf_new.info() |
Выход : 
As we can see in the output, column “A” and “C” are of object type even though they contain integer value. So, let’s try the infer_objects() function.
# applying infer_objects() function.df_new = df_new.infer_objects() # Print the dtype after applying the functiondf_new.info() |
Output :
Now, if we look at the dtype of each column, we can see that the column “A” and “C” are now of int64 type.
Example #2: Use infer_objects() function to infer better data type for the object.
# importing pandas as pdimport pandas as pd # Creating the dataframe df = pd.DataFrame({"A":["sofia", 5, 8, 11, 100], "B":[2 + 2j, 8, 77, 4, 11], "C":["amy", 11, 4, 6, 9]}) # Print the dataframedf |
Let’s see the dtype (data type) of each column in the dataframe.
# to print the basic infodf.info() |
As we can see in the output, first and third column is of object type. whereas the second column is of complex128 type. Now slice the dataframe and create a new dataframe from it.
# slice from the 1st row till enddf_new = df[1:] # Let"s print the new data framedf_new # Now let"s print the data type of the columnsdf_new.info() |
As we can see in the output, column “A” and “C” are of object type even though they contain integer value. Similar is the case with column “B”. So, let’s try the infer_objects() function.
# applying infer_objects() function.df_new = df_new.infer_objects() # Print the dtype after applying the functiondf_new.info() |
Выход :
Notice, the dtype for column “B” did not change. infer_objects() function tries to do soft conversion leaving non-object and unconvertible columns unchanged.
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