Condition to group rows in dataframes python into an interval

I succeeded to extract data into a dataframe, but I don't know how can I manage it to detect values which are in the same range and mean them.

Here is a simple version of my data:

import pandas as pd

matrix = [[0,11,0],[0.99,25,1],[1.99,17,1],[0.1,6,0],[0.88,9,1]	]

data=pd.DataFrame(matrix,columns=['distance in mm','Lithium','Calcium'])

Then I want to group in another dataframe rows that have a common distance in mm of +/- 0.2 mm, calculate the average and standard deviation.

I wish an output similar to this:

Should I create a dictionary which will contain mean and std deviation values?

In the dataframe call data you can find that the first column is named 'distance in mm'. I would like to measure the interval of each single row and unify the row that share the same interval by calculating the mean and the standard deviation. I tried : data['distance_bins'] = pd.cut(data['distance in mm'], np.arange(0, data['distance in mm'].max() + 0.2, 0.2))

The idea consists to group data (analytical replicates) not using labelling but relative position on a sample.

Then I tried: groups = data.groupby('distance_bins'). But after that I'm completely lost, since I am not familiar with object which has been created by the function groupby – <class 'pandas.core.series.Series'>.

Try this -

1. Create your bins based on the minimum and maximum values in the first column
2. Next, your pd.cut to cut your column based on the bins. In this case, you are including left and excluding right [left, right)
3. Then, define your aggregations in a dictionary and groupby your data based on the groups defined above, while passing the aggregations to .agg() method.
4. Finally, (Optionally), filter out the rows with nan values based on first column. You can also rename the index for readibility.

import pandas as pd

# Create bins with +/- 0.2
mini = data['distance in mm'].min()
maxi = data['distance in mm'].max()
bins = np.arange(mini, maxi+0.4, 0.4)

# Cut into groups
groups = pd.cut(data['distance in mm'], bins, right=False)

# Aggregate using groupby
aggr = {'distance in mm':['mean', 'std'], 
        'Lithium':['mean','std'], 
        'Calcium':['mean', 'std']}
grouped = data.groupby(groups).agg(aggr)

# Filter out rows with nans in first column (OPTIONAL)
filtered = grouped[grouped[('distance in mm','mean')].notna()] #<-- Optional
filtered.index.name = 'bins'                                   #<-- Optional

print(filtered)

           distance in mm           Lithium            Calcium     
                     mean       std    mean        std    mean  std
bins                                                               
[0.0, 0.4)          0.050  0.070711     8.5   3.535534     0.0  0.0
[0.8, 1.2)          0.935  0.077782    17.0  11.313708     1.0  0.0
[1.6, 2.0)          1.990       NaN    17.0        NaN     1.0  NaN