Teradata Python Package Function Reference - to_pandas - Teradata Python Package - Look here for syntax, methods and examples for the functions included in the Teradata Python Package.
Teradata® Python Package Function Reference
- Product
- Teradata Python Package
- Release Number
- 16.20
- Published
- February 2020
- Language
- English (United States)
- Last Update
- 2020-07-17
- lifecycle
- previous
- Product Category
- Teradata Vantage
- teradataml.dataframe.dataframe.DataFrame.to_pandas = to_pandas(self, index_column=None, num_rows=99999)
- DESCRIPTION:
Returns a Pandas DataFrame for the corresponding teradataml DataFrame Object.
PARAMETERS:
index_column:
Optional Argument.
Specifies column(s) to be used as Pandas index.
When the argument is provided, the specified column is used as the Pandas index.
Otherwise, the teradataml DataFrame's index (if exists) is used as the Pandas index
or the primary index of the table on Vantage is used as the Pandas index.
The default integer index is used if none of the above indexes exists.
Default Value: Integer index
Types: str OR list of Strings (str)
num_rows:
Optional Argument.
The number of rows to retrieve from DataFrame while creating Pandas Dataframe.
Default Value: 99999
Types: int
RETURNS:
Pandas DataFrame
Note:
Column types of the resulting Pandas DataFrame depends on pandas.read_sql_query().
RAISES:
TeradataMlException
EXAMPLES:
Teradata supports the following formats:
A] No parameter(s): df.to_pandas()
B] Single index_column parameter: df.to_pandas(index_column = "col1")
C] Multiple index_column (list) parameters: df.to_pandas(index_column = ['col1', 'col2'])
D] Only num_rows parameter specified: df.to_pandas(num_rows = 100)
E] Both index_column & num_rows specified: df.to_pandas(index_column = 'col1', num_rows = 100)
Column names ("col1", "col2"..) are strings representing Teradata Vantage table Columns.
It supports all standard Teradata data types for columns: INTEGER, VARCHAR(5), FLOAT etc.
df is a Teradata DataFrame object: df = DataFrame.from_table('admissions_train')
>>> load_example_data("dataframe","admissions_train")
>>> df = DataFrame("admissions_train")
>>> df
masters gpa stats programming admitted
id
22 yes 3.46 Novice Beginner 0
37 no 3.52 Novice Novice 1
35 no 3.68 Novice Beginner 1
12 no 3.65 Novice Novice 1
4 yes 3.50 Beginner Novice 1
38 yes 2.65 Advanced Beginner 1
27 yes 3.96 Advanced Advanced 0
39 yes 3.75 Advanced Beginner 0
7 yes 2.33 Novice Novice 1
40 yes 3.95 Novice Beginner 0
>>> pandas_df = df.to_pandas()
>>> pandas_df
masters gpa stats programming admitted
id
15 yes 4.00 Advanced Advanced 1
14 yes 3.45 Advanced Advanced 0
31 yes 3.50 Advanced Beginner 1
29 yes 4.00 Novice Beginner 0
23 yes 3.59 Advanced Novice 1
21 no 3.87 Novice Beginner 1
17 no 3.83 Advanced Advanced 1
34 yes 3.85 Advanced Beginner 0
13 no 4.00 Advanced Novice 1
32 yes 3.46 Advanced Beginner 0
11 no 3.13 Advanced Advanced 1
...
>>> pandas_df = df.to_pandas(index_column = 'id')
>>> pandas_df
masters gpa stats programming admitted
id
15 yes 4.00 Advanced Advanced 1
14 yes 3.45 Advanced Advanced 0
31 yes 3.50 Advanced Beginner 1
29 yes 4.00 Novice Beginner 0
23 yes 3.59 Advanced Novice 1
21 no 3.87 Novice Beginner 1
17 no 3.83 Advanced Advanced 1
34 yes 3.85 Advanced Beginner 0
13 no 4.00 Advanced Novice 1
32 yes 3.46 Advanced Beginner 0
11 no 3.13 Advanced Advanced 1
28 no 3.93 Advanced Advanced 1
...
>>> pandas_df = df.to_pandas(index_column = 'gpa')
>>> pandas_df
id masters stats programming admitted
gpa
4.00 15 yes Advanced Advanced 1
3.45 14 yes Advanced Advanced 0
3.50 31 yes Advanced Beginner 1
4.00 29 yes Novice Beginner 0
3.59 23 yes Advanced Novice 1
3.87 21 no Novice Beginner 1
3.83 17 no Advanced Advanced 1
3.85 34 yes Advanced Beginner 0
4.00 13 no Advanced Novice 1
3.46 32 yes Advanced Beginner 0
3.13 11 no Advanced Advanced 1
3.93 28 no Advanced Advanced 1
...
>>> pandas_df = df.to_pandas(index_column = ['masters', 'gpa'])
>>> pandas_df
id stats programming admitted
masters gpa
yes 4.00 15 Advanced Advanced 1
3.45 14 Advanced Advanced 0
3.50 31 Advanced Beginner 1
4.00 29 Novice Beginner 0
3.59 23 Advanced Novice 1
no 3.87 21 Novice Beginner 1
3.83 17 Advanced Advanced 1
yes 3.85 34 Advanced Beginner 0
no 4.00 13 Advanced Novice 1
yes 3.46 32 Advanced Beginner 0
no 3.13 11 Advanced Advanced 1
3.93 28 Advanced Advanced 1
...
>>> pandas_df = df.to_pandas(index_column = 'gpa', num_rows = 3)
>>> pandas_df
id masters stats programming admitted
gpa
3.46 22 yes Novice Beginner 0
2.33 7 yes Novice Novice 1
3.95 40 yes Novice Beginner 0