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- lpad(source_string, length, fill_string)
- DESCRIPTION:
Function returns the source_string padded to the left with the characters
in fill_string so that the resulting string has 'length' characters.
PARAMETERS:
source_string:
Required Argument.
Specifies a ColumnExpression of a string column or a string literal
to be padded. If the length of source_string is greater than 'length',
source_string is truncated to 'length' characters.
Format of a ColumnExpression of a string column: '<dataframe>.<dataframe_column>.expression'.
Supported column types: VARCHAR, or CLOB
length:
Required Argument.
Specifies a ColumnExpression of an int column or an integer literal specifying
the number of characters in the resulting string.
Format of a ColumnExpression of a string column: '<dataframe>.<dataframe_column>.expression'.
Supported column types: INTEGER, BIGINT, or NUMBER
fill_string:
Optional Argument.
Specifies a ColumnExpression of a string column or a string literal
used to pad the source_string.
The sequence of characters in fill_string is replicated as necessary.
Format of a ColumnExpression of a string column: '<dataframe>.<dataframe_column>.expression'.
Supported column types: CHAR, VARCHAR, or CLOB
NOTE:
Function accepts positional arguments only.
EXAMPLES:
# Load the data to run the example.
>>> load_example_data("dataframe", "admissions_train")
>>>
# Create a DataFrame on 'admissions_train' table.
>>> admissions_train = DataFrame("admissions_train")
>>> admissions_train
masters gpa stats programming admitted
id
22 yes 3.46 Novice Beginner 0
36 no 3.00 Advanced Novice 0
15 yes 4.00 Advanced Advanced 1
38 yes 2.65 Advanced Beginner 1
5 no 3.44 Novice Novice 0
17 no 3.83 Advanced Advanced 1
34 yes 3.85 Advanced Beginner 0
13 no 4.00 Advanced Novice 1
26 yes 3.57 Advanced Advanced 1
19 yes 1.98 Advanced Advanced 0
>>>
# Import func from sqlalchemy to execute lpad function.
>>> from sqlalchemy import func
# Example 1: Pad string in "stats" column with 0.
# Create a sqlalchemy Function object.
>>> lpad_func_ = func.lpad(admissions_train.stats.expression, 10, "0")
>>>
# Pass the Function object as input to DataFrame.assign().
>>> df = admissions_train.assign(lpad_gpa_=lpad_func_)
>>> print(df)
masters gpa stats programming admitted lpad_gpa_
id
15 yes 4.00 Advanced Advanced 1 00Advanced
34 yes 3.85 Advanced Beginner 0 00Advanced
13 no 4.00 Advanced Novice 1 00Advanced
38 yes 2.65 Advanced Beginner 1 00Advanced
5 no 3.44 Novice Novice 0 0000Novice
40 yes 3.95 Novice Beginner 0 0000Novice
7 yes 2.33 Novice Novice 1 0000Novice
22 yes 3.46 Novice Beginner 0 0000Novice
26 yes 3.57 Advanced Advanced 1 00Advanced
17 no 3.83 Advanced Advanced 1 00Advanced
>>>
# Example 2: Pad string in "stats" column with strings from "masters" column.
# Create a sqlalchemy Function object.
>>> lpad_func_ = func.lpad(admissions_train.stats.expression, 20, admissions_train.masters.expression)
>>>
# Pass the Function object as input to DataFrame.assign().
>>> df = admissions_train.assign(lpad_gpa_=lpad_func_)
>>> print(df)
masters gpa stats programming admitted lpad_gpa_
id
13 no 4.00 Advanced Novice 1 nonononononoAdvanced
26 yes 3.57 Advanced Advanced 1 yesyesyesyesAdvanced
5 no 3.44 Novice Novice 0 nononononononoNovice
19 yes 1.98 Advanced Advanced 0 yesyesyesyesAdvanced
15 yes 4.00 Advanced Advanced 1 yesyesyesyesAdvanced
40 yes 3.95 Novice Beginner 0 yesyesyesyesyeNovice
7 yes 2.33 Novice Novice 1 yesyesyesyesyeNovice
22 yes 3.46 Novice Beginner 0 yesyesyesyesyeNovice
36 no 3.00 Advanced Novice 0 nonononononoAdvanced
38 yes 2.65 Advanced Beginner 1 yesyesyesyesAdvanced
>>>
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