I'm fairly new to Alteryx, and I'm seeking assistance regarding the dataset I'm working with. I'm uncertain whether the tasks I have in mind can be accomplished using Alteryx. Your guidance would be greatly appreciated.
First Step: My initial focus involves the removal of the '#' character from the 'Address' column, as part of a comprehensive data cleanup process.
Second Step: In the subsequent phase, I'm aiming to maintain the integrity of entries commencing with alphabetic characters, ensuring they remain unaltered in the output.
Third Step: Following that, my objective is to streamline the dataset further by excluding entries that solely consist of numbers or commence with numbers (e.g., 03-204 / 56 / 54A-4 / 14B). This refinement will yield 'null' as the output for these cases.
Fourth Step: An additional refinement entails extracting text from entries that initiate with a numerical value followed by text. I'm specifically interested in retrieving the text after the first space.
Last Step: While I'm pursuing these refinements, I'm unsure about the feasibility of this approach in Alteryx. It's also worth noting that I'm considering the retention of text that concludes with a whole number. Conversely, text concluding with a number that incorporates alphabets or symbols (e.g., 112B / 11-63) should be removed.
Once these steps are completed, hope the outcome will same as 'New_Address'.
I truly appreciate your insights and guidance on this matter. Your expertise is of immense value.
Address | New_Address |
56 | |
#03-204 | |
22 DUNEARN ROAD | DUNEARN ROAD |
WOODLANDS STREET 13 | WOODLANDS STREET 13 |
#158 CANBERRA DRIVE 06-35 | CANBERRA DRIVE |
#ONE-NORTH GATEWAY | ONE-NORTH GATEWAY |
509A YISHUN AVENUE 4 | YISHUN AVENUE 4 |
31#07-02 ALEXANDRA ROAD | ALEXANDRA ROAD |
Solved! Go to Solution.
I extend my heartfelt gratitude for your invaluable assistance. I am pleased to share that your guidance has enabled successful implementation. Thank you immensely for your support.