使用 for 循环替换 Pandas 中每一行和每一列的单元格值

Question

I'm using python with pandas, but can also import any other library Mu dataset has missing values (NaN) in thousands of rows in each column.

Examle

**Name,Type,Region...**
Oranges,Fruit,Western Europe  
NaN,NaN,NaN  
NaN,NaN,NaN  
Blueberry, berry,Easter Europe  
NaN,NaN,NaN 
Raspberry, berry,Easter Europe
NaN,NaN,NaN
NaN,NaN,NaN 

we can assume that the values in cells that have NaN can be re written to be the same as the previous value, until a new non NaN value is reached. Example:

**Name,Type,Region...**
Oranges,Fruit,Western Europe  
Oranges,Fruit,Western Europe 
Oranges,Fruit,Western Europe 
Blueberry, berry,Easter Europe  
Blueberry, berry,Easter Europe 
Raspberry, berry,Easter Europe  
Raspberry, berry,Easter Europe  
Raspberry, berry,Easter Europe

How can I iterate over each row value and each column to re-write the NaN values to match the first Non NaN value before it?

Rules: if cell = NaN and previous_cell = not NaN, replace value with previous_cell, if cell = NaN and previous_cell = NaN, continue (eliminating edge case when the whole column is empty) if cell = NaN, continue

I have a huge dataset, so this is not possible to do manually in the CSV file itself

Nested query which does not work

Answer 1

you can use apply with ffill for all clomuns it avaliable in pandas:

df.apply(lambda x: x.fillna(df['Name'].shift())).ffill()
df.apply(lambda x: x.fillna(df['Type'].shift())).ffill()
df.apply(lambda x: x.fillna(df['Region'].shift())).ffill()