I have a dataframe in pandas. I have done the followings:
data.reset_index(inplace=True)
data.set_index(['Date','Name'],inplace=True)
How can I get the index (i.e., Date and Name) for a specific row (e.g., for data.iloc[0])
Related
I have to format a dataframe so some code can receive it. It has to be in a specific format. The raw dataframe produced gives me a multi-index format. When I pass to the code it gives an IndexError because it was expecting 1 index.
Raw dataframe
I make a copy of the dataframe and remove the index.
ticker_data_2 = ticker_data.copy().reset_index()
Removed index
I need the timestamp column to be the index, so I reset the index to be timestamp. But now I have 2 columns named timestamp. Set index is supposed to remove the timestamp column and place it as the index, not make a copy.
ticker_data_2.set_index(ticker_data_2['timestamp'], inplace=True)
Duplicate timestamp columns
How do I fix to make it so only timestamp shows as the index, and not have a second timestamp column.
I'm working on a jupyter notebook, and I would like to get the average 'pcnt_change' based on 'day_of_week'. How do I do this?
A simple groupby call would do the trick here.
If df is the pandas dataframe:
df.groupby('day_of_week').mean()
would return a dataframe with average of all numeric columns in the dataframe with day_of_week as index. If you want only certain column(s) to be returned, select only the needed columns on the groupby call (for e.g.,
df[['open_price', 'high_price', 'day_of_week']].groupby('day_of_week').mean()
I have a dataframe at hourly level with several columns. I want to extract the entire rows (containing all columns) of the 10 top values of a specific column for every year in my dataframe.
so far I ran the following code:
df = df.groupby([df.index.year])['totaldemand'].apply(lambda grp: grp.nlargest(10)))
The problem here is that I only get the top 10 values for each year of that specific column and I lose the other columns. How can I do this operation and having the corresponding values of the other columns that correspond to the top 10 values per year of my 'totaldemand' column?
We usually do head after sort_values
df = df.sort_values('totaldemand',ascending = False).groupby([df.index.year])['totaldemand'].head(10)
nlargest can be applied to each group, passing the column to look for
largest values.
So run:
df.groupby([df.index.year]).apply(lambda grp: grp.nlargest(3, 'totaldemand'))
Of course, in the final version replace 3 with your actual value.
Get the index of your query and use it as a mask on your original df:
idx = df.groupby([df.index.year])['totaldemand'].apply(lambda grp: grp.nlargest(10))).index.to_list()
df.iloc[idx,]
(or something to that extend, I can't test now without any test data)
I am selecting row by row as follows:
for i in range(num_rows):
row = df.iloc[i]
as a result I am getting a Series object where row.index.values contains names of df columns.
But I wanted instead dataframe with only one row having dataframe columns in place.
When I do row.to_frame() instead of 1x85 dataframe (1 row, 85 cols) I get 85x1 dataframe where index contains names of columns and row.columns
outputs
Int64Index([0], dtype='int64').
But all I want is just original data-frame columns with only one row. How do I do it?
Or how do I convert row.index values to row.column values and change 85x1 dimension to 1x85
You just need to adding T
row.to_frame().T
Also change your for loop with adding []
for i in range(num_rows):
row = df.iloc[[i]]
I have a pandas dataframe with 10 columns. I would like to add a column which will uniquely identify every row. I do have to come up with the unique value(could be as simple as a running sequence). How can I do this? I tried adding index as a column itself but for some reason I get a KeyError when I do this.
add a column from range of len of you index
df['new'] = range(1, len(df.index)+1)