I am new to python and I was successful in using apply in a dataframe to create a new column inside a dataframe.
X['Geohash']=X[['Lat','Long']].apply (lambda column: geohash.encode(column[0],column[1],precision=8), axis=1)
this is calling the geohash function with the latitudes and longitudes per row and per column.
Now I have two new data frames one for Latitude and one for Longitude.
Each dataframe has twenty columns and I want that the
.apply (lambda column: geohash.encode(column[0],column[1],precision=8), axis=1)
is called twenty times.
-First time the first
dataframe-Latitude column with the first dataframe-Longitude column then
-the second time, the second dataframe-Latitude column with the second dataframe-Longitude column.
How I can do this iteration per column and at each iteration call the
.apply (lambda column: geohash.encode(column[0],column[1],precision=8), axis=1)
What I want to get is a new dataframe with twenty columns with each column being the result of the geohash function.
Ideas will be appreciated.
You can do this by creating an "empty" dataframe with 20 columns, and then using df.columns[i] to loop through your other dataframes - something like this:
output = pd.DataFrame({i:[] for i in range(20)})
This creates an empty dataframe with all the columns you wanted (numbered).
Now, let's say longitude and latitude dataframes are called 'lon' and 'lat'. We need to join them into one dataframe Then:
lonlat = lat.join(lon)
for i in range(len(output.columns)):
output[output.columns[i]] = lonlat.apply(lambda column: geohash.encode(column[lat.columns[i]],
column[lon.columns[i]],
precision=8), axis=1)
Related
I have a dataframe with multiple columns as t_orno,t_pono, t_sqnb ,t_pric,....and so on(it's a table with multiple columns).
The 2nd dataframe contains certain name of the columns from 1st dataframe. Eg.
columnname
t_pono
t_pric
:
:
I need to select only those columns from the 1st dataframe whose name is present in the 2nd. In above example t_pono,t_pric.
How can this be done?
Let's say you have the following columns (which can be obtained using df.columns, which returns a list):
df1_cols = ["t_orno", "t_pono", "t_sqnb", "t_pric"]
df2_cols = ["columnname", "t_pono", "t_pric"]
To get only those columns from the first dataframe that are present in the second one, you can do set intersection (and I cast it to a list, so it can be used to select data):
list(set(df1_cols).intersection(df2_cols))
And we get the result:
["t_pono", "t_pric"]
To put it all together and select only those columns:
select_columns = list(set(df1_cols).intersection(df2_cols))
new_df = df1.select(*select_columns)
I have a big dataframe its about 200k of rows and 3 columns (x, y, z). Some rows doesn't have y,z values and just have x value. I want to make a new column that first set of data with z value be 1,second one be 2,then 3, etc. Or make a multiIndex same format.
Following image shows what I mean
Like this image
I made a new column called "NO." and put zero as initial value. Then
I tried to record the index of where I want the new column get a new value. with following code
df = pd.read_fwf(path, header=None, names=['x','y','z'])
df['NO.']=0
index_NO_changed = df.index[df['z'].isnull()]
Then I loop through it and change the number:
for i in range(len(index_NO_changed)-1):
df['NO.'].iloc[index_NO_changed[i]:index_NO_changed[i+1]]=i+1
df['NO.'].iloc[index_NO_changed[-1]:]=len(index_NO_changed)
But the problem is I get a warning that "
A value is trying to be set on a copy of a slice from a DataFrame
I was wondering
Is there any better way? Is creating multiIndex instead of adding another column easier considering size of dataframe?
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 tried to perform my self-created function on a for loop.
Some remarks in advance:
ma_strategy is my function and requires three inputs
ticker_list is a list with strings result is a pandas Dataframe with 7 columns and I can call the column 'return_cum' with result['return_cum']. - The rows of this column are containing floating point numbers.
My intention is the following:
The for loop should iterate over the items in my ticker_list and should save the 'return_cum' columns in a DataFrame. Then the different 'return_cum' columns should be stored together so that at the end I get a DataFrame with all the 'return_cum' columns of my ticker list.
How can I achieve that goal?
My approach is:
for i in ticker_list:
result = ma_strategy(i, 20, 5)
x = result['return_cum'].to_frame()
But at this stage I need some help.
If i inderstood you correctly this should work:
result_df =pd.DataFrame()
for i in ticker_list:
result= ma_strategy(i, 20,5)
resault_df[i + '_return_cum'] = result['return_cum']