How can I get the data from this dataframe into 2 rows only, deleting the NaN? (I concated 3 different Dataframes into a new one, showing averages from another Dataframe)enter image description here
This is what i want to achieve:
0 Bitcoin (BTC) 36568.673315 5711.3.059220. 1.229602e+06
1 Ethereum (ETH) 2550.870272 670225.756425 8.806719e+05
It can either be in a new dataframe or using the old one. Thank you so much for ur help :)
Try this:
df.bfill(axis ='rows', inplace=True) # filling the missing values
df.dropna(inplace=True) # drop rows with Nulls
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I am trying to format a data frame from 2 rows to 1 rows. but I am encountering some issues. Do you have any idea on how to do that? Here the code and df:
Thanks!
If you are looking to convert two rows into one, you can do the following...
Stack the dataframe and reset the index at level=1, which will convert the data and columns into a stack. This will end up having each of the column headers as a column (called level_1) and the data as another column(called 0)
Then set the index as level_1, which will move the column names as index
Remove the index name (level_1). Then transpose the dataframe
Code is shown below.
df3=df3.stack().reset_index(level=1).set_index('level_1')
df3.index.name = None
df3=df3.T
Output
df3
I need help getting two rows in the same datafram merged/joined.
The first table is the df that I have right now
The second one is the one that I would like to have
I need to combine Jim and Bill. I don't want to overwrite values in either tables. I just want to update NaN values in the row (Bill) with the values with row(Jim) e.g city
There are about 20 columns that I need updating because of that I cannot just update the Bill/City cell
Thanks
You can try
df.loc['Bill'] = df.loc['Bill'].fillna(df.loc['Jim'])
# or
df.loc['Bill'].fillna(df.loc['Jim'], inplace=True)
I'm facing a strange issue in which I'm trying to replace all NaN values in a dataframe with values taken from another one (same length) that has the relevant values.
Here's a glimpse for the "target dataframe" in which I want to replace the values:
data_with_null
Here's the dataframe where I want to take data from: predicted_paticipant_groups
I've tried:
data_with_null.participant_groups.fillna(predicted_paticipant_groups.participant_groups, inplace=True)
but it just fills all values NaN values with the 1st one (Infra)
Is it because of the indexes of data_with_null are all zeros?
Reset the index and try again.
data_with_null.reset_index(drop=True, inplace=True)
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Hi Dear Friends,
I have a little problem while trying to pivot my dataframe
I tried to put the column 0 as the columns and the column 1 as values.
But that doesn't give me what i expected.
I don't know why there are NaN everywhere
Anyone can help me to structure this?
Thank you
It look like that pivot() with setting only columns= and values= creates a cross table with index (0,1,2,…) and the column.
How about preparing new columns with same value and setting the column to index= of pivot() as following?
columns1['tmp'] = 0
columns1.pivot(columns=0 , values=1 , index='tmp').reset_index(drop=True)
I have a dataframe df with two columns date and data. I want to take the first difference of the data column and add it as a new column.
It seems that df.set_index('date').shift() or df.set_index('date').diff() give me the desired result. However, when I try to add it as a new column, I get NaN for all the rows.
How can I fix this command:
df['firstdiff'] = df.set_index('date').shift()
to make it work?