How to get all the duplicate values of one specific column in dataframe?
I want to only check values on one column but it's getting output with table or data.
I want to count the number the times each value is repeated
Use df['column'].value_counts().
Above answer is correct , and further can be converted to dict
df["col1"].value_counts().to_dict()
Related
I want to check how many values are lower than 2500
1)Using .count(
df[df.price<2500]["price"].count()
Using .values_counts()
df[df.price<2500]["price"].value_counts()
this ise code view
First one results 27540 and second 2050. Which one is correct count?
Definitely not 2050, analyze your histogram.
The method value_counts will assign only one row for a number that has duplicates but it will associate the number of duplicates. So it seems to be 2050 differents prices, but if you count duplicates there are much more.
I have this dataframe and I want to add a column to it with the total of distinct SalesOrderId for a given CustomerId
So, with I am trying to do there would be a new column with the value 3 for all this rows.
How can I do it?
I am trying this way but I get an error
data['TotalOrders'] = data.groupby([['CustomerID','SalesOrderID']]).size().reset_index(name='count')
Try using transform:
data['TotalOrders'] = df.groupby('CustomerID')['SalesOrderID'].transform('nunique')
This will give you one entry for each entry in the group. (thanks #Rodalm)
I have a dataframe where I want to extract values from two columns but the criteria set is unique values from one of the columns. In the image below, I want to extract unique values of 'education' along with its corresponding values from 'education-num'. I can easily extract the unique values with df['education'].unique() and I am stuck with not being able to extract the 'education-num'.
image of the dataframe.
(Originally the task was to compute the population of people with education of Bachelors, Masters and Doctorate and I assume this would be easier when comparing the 'education-num' rather than logical operators on string. But if there's any way we could do it directly from the 'education' that would also be helpful.
Edit: Turns out the Dataframe.isin helps to select rows by the list of string as given in the solution here.)
P.S. stack-overflow didn't allow me to post the image directly and posted a link to it instead...😒
Select columns by subset and call DataFrame.drop_duplicates:
df1 = df[['education', 'education-num']].drop_duplicates()
If need count population use:
df2 = df.groupby(['education', 'education-num']).size().reset_index(name='count')
I'm new to Pandas.
I've got a dataframe where I want to group by user and then find their lowest score up until that date in the their speed column.
So I can't just use df.groupby(['user'])['speed'].transform('min) as this would give the min of all values not just form the current row to the first.
What can I use to get what I need?
Without seeing your dataset it's hard to help you directly. The problem does boil down to the following. You need to select the range of data you want to work with (so select rows for the date range and columns for the user/speed).
That would look something like x = df.loc[["2-4-2018","2-4-2019"], ['users', 'speed']]
From there you could do a simple x['users'].min() for the value or x['users'].idxmin() for the index of the value.
I haven't played around for a bit with Dataframes, but you're looking for how to slice Dataframes.
I am currently using Python 2.7. I currently have three columns in an Excel document all with different integer values in. The amount of values can vary ranging from 10 through to thousands. Basically, what I am looking to do is scan through the column one and compare each value to see if any appear in column two and three. Similarly, I will then do the same with column 2 to see if any appear in column one and three etc....
My thinking on this is to populate the content of each column into a respective list and then iterate over list 1 (column 1) and then run an if statement to compare each iteration value and see if it exists in list 2 (column 2).
My question is, is this the most efficient means of running this comparison? As said, within the three columns, the same number should appear in each of the three columns (it may appear on a number of occasions) and so I'm looking to identify those numbers which appear in each of the three columns.
Thanks
What about using set intersection?
set(column_1_vals) & set(column_2_vals) & set(column_3_vals)
That will give you those values which appear in all three columns.