Issue
I've found a behavior in pandas DataFrames that I don't understand.
df = pd.DataFrame(np.random.randint(1, 10, (3, 3)), index=['one', 'one', 'two'], columns=['col1', 'col2', 'col3'])
new_data = pd.Series({'col1': 'new', 'col2': 'new', 'col3': 'new'})
df.iloc[0] = new_data
# resulting df looks like:
# col1 col2 col3
#one new new new
#one 9 6 1
#two 8 3 7
But if I try to add a dictionary instead, I get this:
new_data = {'col1': 'new', 'col2': 'new', 'col3': 'new'}
df.iloc[0] = new_data
#
# col1 col2 col3
#one col2 col3 col1
#one 2 1 7
#two 5 8 6
Why is this happening? In the process of writing up this question, I realized that most likely df.loc is only taking the keys from new_data, which also explains why the values are out of order. But, again, why is this the case? If I try to create a DataFrame from a dictionary, it handles the keys as if they were columns:
pd.DataFrame([new_data])
# col1 col2 col3
#0 new new new
Why is that not the default behavior in df.loc?
Solution
It's the difference between how a dictionary iterates and how a pandas series is treated.
A pandas series matches it's index to columns when being assigned to a row and matches to index if being assigned to a column. After that, it assigns the value that corresponds to that matched index or column.
When an object is not a pandas object with a convenient index object to match off of, pandas will iterate through the object. A dictionary iterates through it's keys and that's why you see the dictionary keys in that rows slots. Dictionaries are not sorted and that's why you see shuffled keys in that row.
Answered By - piRSquared
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