What is the suggested way to iterate over the rows in pandas like you would in a file? For example:
LIMIT = 100
for row_num, row in enumerate(open('file','r')):
print (row)
if row_num == LIMIT: break
I was thinking to do something like:
for n in range(LIMIT):
print (df.loc[n].tolist())
Is there a built-in way to do this though in pandas?
Hasn't anyone answered the simple solution?
for row in df.head(5).itertuples():
# do something
Take a peek at this post.
I know others have suggested iterrows but no-one has yet suggested using iloc combined with iterrows. This will allow you to select whichever rows you want by row number:
for i, row in df.iloc[:101].iterrows():
print(row)
Though as others have noted if speed is essential an apply function or a vectorized function would probably be better.
>>> df
a b
0 1.0 5.0
1 2.0 4.0
2 3.0 3.0
3 4.0 2.0
4 5.0 1.0
5 6.0 NaN
>>> for i, row in df.iloc[:3].iterrows():
... print(row)
...
a 1.0
b 5.0
Name: 0, dtype: float64
a 2.0
b 4.0
Name: 1, dtype: float64
a 3.0
b 3.0
Name: 2, dtype: float64
>>>
You have values, itertuples and iterrows out of which itertuples performs best as benchmarked by fast-pandas.
You can use iterools.islice to take the first n items from iterrows:
import itertools
limit = 5
for index, row in itertools.islice(df.iterrows(), limit):
...
Since you said that you want to use something like an if I would do the following:
limit = 2
df = pd.DataFrame({"col1": [1,2,3], "col2": [4,5,6], "col3": [7,8,9]})
df[:limit].loc[df["col3"] == 7]
This would select the first two rows of the data frame, then return the rows out of the first two rows that have a value for the col3 equal to 7. Point being you want to use iterrows only in very very specific situations. Otherwise, the solution can be vectorized.
I don't know what exactly are you trying to achieve so I just threw a random example.
If you must iterate over the dataframe, you should use the iterrows() method:
for index, row in df.iterrows():
...