Pandas Conditional Columns: Set Pandas Conditional Column Based on Values of Another Column
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- เผยแพร่เมื่อ 8 ก.ค. 2024
- In this video, you'll learn how to set a Pandas column values based on values of another column. You'll learn how to set values based on a single condition, multiple conditions, and emulating a VLOOKUP in Python.
To see the written tutorial and all the sample code, check out my website here: datagy.io/pandas-conditional-...
0:00 Introduction
0:55 Using loc to Set Conditional Values
3:50 Using Numpy Select
6:23 Pandas Map a Dictionary
9:00 Pandas Apply a Custom Function
12:20 Conclusion
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I spend two days looking for a solution for a conditional problem. This video was exactly what I needed. Thank you very much!!!
Thanks so much for the feedback!!
There are SO many, quite frankly TOO many instructional videos, and they are tedious, too long, and unclear, but YOURS is EXACTLY on point
Thank you so much! I really appreciate it.
Guys he is the best and simplest , best one seriously , just trust and watch till the end .
I've been looking for a video to help with my code for days. Your vid was by far the best and only one that added value. Well done!
Thanks so much, Andrew!
The numpy option was exactly what I needed and worked great. Thank you, also for the other videos. Keep up the good work…
Thanks so much for your feedback! 😊
For me too
Great presntation. I am building a trading bot using algorythm that deals with conditional columns (candlestick lengths of previous columbs) This is the video I was looking for. Thanks for the vid. Very clear and informative. Exactly what I was looking for.
This is an excellent Tutorial !!! . Beautifully explained, and you went over issues I have been struggling with. Thank you so much for taking the time to make this video
Thanks so much Ali!
Excellent video! Thanks!!!
Thus video is very helpful and exactly what I needed! Thank you for the multiple examples with different scenarios!
Thank you so much!
Thank you bro that what i was looking for my problem to be solved. Great!!!!
great explanation, used the numpy method. works brilliantly
Thanks so much!
Excellent video! Just what I need to solve a problem. Thank you!
Thank you!! I appreciate the comment!
This was a clear and concise explanation that helped me a lot. Thank you for all that you do!
Thank you SO much David! That was very generous of you! I’m so glad you liked the video!
One of the best I had seen.
your video is so good, thank u so much I was looking for this everywhere :)
Thank you! That means a lot!
This is really awesome. Thankyou for this tutorial.
Thanks so much!
Oh man -- I've been trying to figure out more efficient ways of defining bins for a df. While I'm fairly new to Python, I've coded (in SAS) for 15 years, but that experience is only helpful in knowing how I would solve a coding problem -- in SAS.
The numpy approach you mentioned is very simple and intuitive -- your tips are going to save me so much time! Thanks for making this video!!!
Thank you so much for this comment Andy! Made my day!
Excellent presentation… you really helped me understand map & apply methods
I'm so glad it was helpful!
Works like a charm! Many thanks dude :)
Thanks Andrés!
heey!! this video provides everything I need for a project I have in mind..
thanks a lot, you saved me a lot of time and effort
I’m so glad!!
Thanks for clear explanation🎉
Thanks Zarir!
Excellent. Thanks for sharing your knowledge.
Thanks Zac!
Excellent
Excellent tutorial nice explanations, concise, covered many related concepts, various ways to do,
Thank you so much!
Thanks for the useful lesson.
I'm glad it was helpful!
That's helpful beginner stuff, Thanks
I’m glad it was helpful!
Thanks man!
Thanks for your comment!
Great video!
Thanks Kenneth!
wow bro. that is very helpful. from indonesia, thank you..
Thanks Firdi!!
Great explanation
Thank you!
Awesome! Could you show an example taking in 2 parameters instead? Say taking Age and Gender... How do we achieve that using the function-apply approach?
great video !
Thank you!!
Can u give me an example if using nested conditional? Because when I have tried my nested conditional with function and loop, the value in new coloumn is nan
This is great. But I need to do this for multiple columns. How do add values from another column to the new column without it affecting the existing values (except where there is null?
Hi, you are doing awesomely work, explaining subject so easily to all, wondaful work, keep it up, could you explain OHLCV data, apply, map functions, user defined functions to to find the trend on the High, Low columns, to get buy signals and sell signals by not using any price lagging indicators.
Superb tutorial. Keep it up
Thanks so much!
@@datagy My pleasure
HI, what if you want to create a similar function with takes 2 values? such that the 2 values will be 2 columns rather than using one column. The def function to take 2 columns instead of one, then use apply function
What about if i have a column that contains several words (on each row) and i need to create a new column with 1s/0s IF ... my records contain a specific word AND (at same time) doesn't contain (another) specific word. I want to flag those records
Hi
how do you replace zero values in a column based on multiple conditions
I have 4 lac records. And I have to assign url based unique ID.. how I can do that?
Hi Thank you. I constantly receive this error, could you please help?
'NAType' object has no attribute 'str'
def SAPID (TotalData1):
if (TotalData1['Type_SubCat']=='Billing'):
return TotalData1['Sales Document']
elif (TotalData1['User Name']=='ANSAZIY'):
return 'OPENNING BALANCE'
elif (TotalData1['Assignment'].str[0]=='4'):
return TotalData1['Assignment'].str[10]
elif (TotalData1['Assignment'].str[0]=='L'):
return TotalData1['Assignment'].str[8]
else:
return TotalData1['Assignment']
It’s likely that you have missing data in your column - you can fill those first before applying the conditional column.
Hi, for example I have a table, which I've got by left outer join:
person - vehicle
dad - car
dad - motorcycle
dad - bicycle
mom - car
mom - bicycle
son - None/NA/NaN/NaT
How to group by person and count with condition (car and motorcycle)?
When I use for example:
df = df.groupby(['person'])['vehicle'].apply(lambda x: x[x == 'car'].count())
But I can't use a list in condition lambda x: x[x in ['car']], pandas says:
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()
I will be very very grateful if you could reply this. i am on a hot seat.
h=1
temp=h
height=[]
for i in range(60):
if a[i]>b[i]:
h=2
temp=h
height.append(h)
elif a[i]
Hey Abhishek, sorry, I’m not sure what you’re hoping to accomplish. Could you elaborate?
@@datagy we have two lists here, list a[] and b[] which contains some values, and the length of each lists is 60 thats why the for loop range is 60, then in the first 'if' condition if the 'i' value of the list a[] is greater then 'i' value of list b[] then the value of h will be 2 and we will append the value of h in the height[] list and similar condition is for second elif condition too but if both the condition fails then the value of h will be whatever the previous value of h, and i used temp variable to accomplish that....so how i will change all these scenarios according to the data frame......where list a and b are now two columns of the dataframe and instead of appending the values of h into a list i want to create a new column in the dataframe which will store it.....i hope that now it clears all the condition ......please help if possible..
it is like how can we compare 2 columns of a dataframe and if possible then how can we assign h the previous value when both the condition fails
@@Gaming_Hub47 Thanks for the explanation! I'm struggling to picture the flow of the data. Can you include the list a and b for me to better understand? Happy to try and help!
@@datagy really sry i missed the point that there is 3 list ....now i corrected the code, there is a list c also ....this problem is so confusing.....i really appreciate that you try to solve my issue........ list a is=[88.85, 88.645, 88.985, 88.775, 88.745, 89.165, 89.495, 88.905, 88.9, 89.015, 89.295, 89.325, 89.375, 89.265, 89.415, 89.465, 89.54, 89.54, 89.24, 89.16, 89.155, 89.155, 89.095, 89.22, 89.2, 89.245, 89.365, 88.935, 89.33, 89.325, 89.185, 88.885, 88.645, 88.65, 88.215, 88.18, 88.575, 88.595, 88.535, 88.585, 88.52, 88.765, 88.97, 88.965, 88.91, 88.69, 88.99, 89.055, 88.75, 88.63, 88.625, 88.39, 88.415, 88.33, 88.76, 88.585, 88.56, 88.85, 88.88, 89.03, 89.575, 89.56, 89.7, 90.395, 90.335, 89.875, 88.845, 88.94, 88.77, 88.61, 88.845, 88.47, 88.615, 88.7, 88.725, 88.9, 88.455, 88.48, 88.655, 88.49, 88.37, 88.535, 88.45, 88.5, 88.885, 88.72, 88.815, 88.905, 88.775, 88.88, 89.14, 89.425, 89.315, 89.005, 89.4, 89.405, 89.38, 89.17, 89.465, 89.19, 89.11, 89.245, 89.28, 89.26, 89.02, 88.865, 88.98, 88.93, 88.87, 88.67, 88.75, 88.895, 88.9, 88.915, 89.15, 89.285, 89.285, 89.05, 88.905, 89.06]
list b is= [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 89.618, 89.668, 89.672, 89.619, 89.523, 89.413, 89.307, 89.226, 89.192, 89.199, 89.241, 89.291, 89.348, 89.384, 89.376, 89.293, 89.16, 88.997, 88.773, 88.609, 88.502, 88.471, 88.498, 88.533, 88.604, 88.709, 88.821, 88.925, 89.006, 89.057, 89.096, 89.122, 89.092, 89.024, 88.935, 88.798, 88.675, 88.608, 88.589, 88.597, 88.65, 88.738, 88.854, 89.111, 89.403, 89.663, 89.975, 90.277, 90.515, 90.62, 90.475, 90.184, 89.776, 89.409, 89.12, 88.852, 88.675, 88.581, 88.594, 88.653, 88.673, 88.694, 88.697, 88.682, 88.663, 88.63, 88.6, 88.659, 88.748, 88.851, 88.956, 89.04, 89.086, 89.164, 89.254, 89.352, 89.427, 89.503, 89.555, 89.59, 89.591, 89.572, 89.555, 89.507, 89.438, 89.371, 89.331, 89.302, 89.242, 89.178, 89.123, 89.07, 89.013, 88.931, 88.87, 88.837, 88.839, 88.892, 88.999, 89.124, 89.238, 89.293, 89.304]
l [nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 89.442, 89.467, 89.441, 89.378, 89.281, 89.179, 89.09, 89.025, 89.008, 89.025, 89.024, 89.004, 89.023, 89.053, 89.05, 88.984, 88.867, 88.667, 88.385, 88.141, 88.009, 87.993, 88.06, 88.162, 88.275, 88.414, 88.563, 88.703, 88.792, 88.833, 88.871, 88.878, 88.837, 88.765, 88.654, 88.532, 88.411, 88.312, 88.262, 88.264, 88.312, 88.427, 88.563, 88.727, 88.939, 89.156, 89.393, 89.688, 89.914, 89.914, 89.713, 89.408, 89.009, 88.664, 88.378, 88.14, 88.032, 88.02, 88.098, 88.203, 88.281, 88.329, 88.371, 88.378, 88.348, 88.312, 88.29, 88.307, 88.375, 88.46, 88.557, 88.655, 88.732, 88.784, 88.873, 88.983, 89.059, 89.101, 89.162, 89.232, 89.269, 89.266, 89.242, 89.187, 89.126, 89.044, 89.003, 88.968, 88.93, 88.891, 88.843, 88.81, 88.758, 88.693, 88.634, 88.598, 88.611, 88.66, 88.759, 88.898, 89.007, 89.069, 89.067]
so these are the 3 lists .....please now try the code with all the values in ....maybe now it is somewhat easy
😃 P R O M O S M!