How to find the last non zero element in every column throughout dataframe?

Question

How can one go about finding the last occurring non zero element in every column of a dataframe?

Input

    A  B
0   0  1
1   0  2
2   9  0
3  10  0
4   0  0
5   0  0

Output

    A  B
0  10  2

Answer 1

You can convert 0 to missing values, use forward filling and select last row by indexing, last cast to integer:

df = df.mask(df==0).ffill().iloc[[-1]].astype(int)
print (df)
    A  B
5  10  2

Answer 2

Here's one approach using ndarray.argmax and advanced indexing:

first_max = df.values[df.ne(0).values.argmax(0), range(df.shape[1])]
out = pd.DataFrame([first_max], columns=df.columns)

---

df = pd.DataFrame({'A': [0,0,0,10,0,0] , 'B': [0,2,0,0,0,0]})

first_max = df.values[df.ne(0).values.argmax(0), range(df.shape[1])]
# array([10,  2])
pd.DataFrame([first_max], columns=df.columns)

    A  B
0  10  2

---

Update

In order to find the last nonzero:

row_ix = df.shape[0]-df.ne(0).values[::-1].argmax(0)-1
first_max = df.values[row_ix, range(df.shape[1])]
out = pd.DataFrame([first_max], columns=df.columns)

Answer 3

Something like:

results = {}
for column in df.columns:
    results[column] = df.loc[df[column]!=0, column].iloc[-1]

This will make a dictionary with all columns as keys and they last non-zero values as values.

EDIT: If you want it in a dataframe, plus dict comprehension for one-liner:

results = pd.DataFrame({column:[df.loc[df[column]!=0, column].iloc[-1]] for column in df.columns})

Answer 4

Loop over the columns then the rows and store the last non zero variable

list = []* number_of_columns
for i in range(len(df)):
    dfcolumn = df[:,i]
    for item in dfcolumn:
        if item !=  0:
            list[i] = [i, item]

print(list)