熊猫以奇怪的格式返回数据（Int64、Float64）

Question

I'm working with pandas, and I need the index of some row where some value is Max. To do this I use seed_row = df[df["veghel_time"] == df.veghel_time.max()] To get the row where the column df["veghel_time"] has the maximum value (details not important).

When I use print(seed_row_df.index) , instead of a regular index value (in this case 126) I get:

Int64Index([126], dtype='int64')

Similarly, print(seed_row_df["veghel_time"]) gives me

126    119
Name: veghel_time, dtype: int64

Instead of simply the value of the column, which is 119 .

Finally, print(seed_row_df["Lat"]) gives:

126    6.57619
Name: Long, dtype: float64

Instead of simply 6.57619

Why is this, and what are the implications of this? Say I want to use these values (For instance sum them). Will this lead to problems? Is there a way to tell pandas that I simply want the values and nothing else?

Answer 1

It's still a single row of a dataframe.

You can use .squeeze() on it (or other types) to get the plain value out if you need it for something non-pandas.

Answer 2

It appears that your result is a tuple represented by the index and value of the data type – perhaps because of some cyclical backend in pandas. A simpler approach would be to query the index of the max value directly:

# gets the index of max time
seed_row_idx = df.veghel_time.idxmax()

# get the value of some column at that index
my_value = df.iloc[seed_row_idx, "Lat"]