[FIXED] pandas: create another data frame using existing data frame from the given index position

Issue

I have a following dataframe where I have one another list of index position based on some condition so just want to create the new dataframe based on the index position and check some condition on that.

df = pd.DataFrame()
df['index'] = [ 0, 28, 35, 49, 85, 105, 208, 386, 419, 512, 816, 888, 914, 989]
df['diff_in_min'] = [ 5, 35, 42, 46, 345, 85, 96, 107, 119, 325, 8, 56, 55, 216]
df['val_1'] = [5, 25, 2, 4, 2, 5, 69, 6, 8, 7, 55, 85, 8, 67]
df['val_2'] = [8, 89, 8, 5, 7, 57, 8, 57, 4, 8, 74, 65, 55, 74]
re_ind = list(np.where(df['diff_in_min'] >= 300))
re_ind = [np.array([85, 512], dtype='int64')]

Just I want to create another dataframe based on re_ind position, ex:

first_df = df[0:85] 
another_df = [85:512] 
last_df =  [512:] 

and each dataframe I want to check one condition

count = 0
temp_df = df[:re_ind[0]]
if temp_df['diff_in_min'].sum() > 500:
    count += 1
temp_df = df[re_ind[0]:re_ind[1]]
if temp_df['diff_in_min'].sum() > 500:
    count += 1
if temp_df = df[re_ind[1]:]
if temp_df['diff_in_min'].sum() > 500:
    count += 1

How can I do that using for loop with creating new data frame using existing dataframe?

Solution

From sample data for groups created by df['diff_in_min'] >= 300) add cumulative sum, then aggregate sum, compare for another condition and count Trues by sum:

s = (df['diff_in_min'] >= 300).cumsum()

out = (df['diff_in_min'].groupby(s).sum() > 500).sum()
print (out)
2

Answered By - jezrael