如何删除熊猫数据框的最后一行数据

I think this should be simple, but I tried a few ideas and none of them worked:

last_row = len(DF)
DF = DF.drop(DF.index[last_row])  #<-- fail!

I tried using negative indices but that also lead to errors. I must still be misunderstanding something basic.

To drop last n rows:

df.drop(df.tail(n).index,inplace=True) # drop last n rows

By the same vein, you can drop first n rows:

df.drop(df.head(n).index,inplace=True) # drop first n rows

DF[:-n]

where n is the last number of rows to drop.

To drop the last row :

DF = DF[:-1]

Since index positioning in Python is 0-based, there won’t actually be an element in index at the location corresponding to len(DF). You need that to be last_row = len(DF) - 1:

In [49]: dfrm
Out[49]: 
          A         B         C
0  0.120064  0.785538  0.465853
1  0.431655  0.436866  0.640136
2  0.445904  0.311565  0.934073
3  0.981609  0.695210  0.911697
4  0.008632  0.629269  0.226454
5  0.577577  0.467475  0.510031
6  0.580909  0.232846  0.271254
7  0.696596  0.362825  0.556433
8  0.738912  0.932779  0.029723
9  0.834706  0.002989  0.333436

[10 rows x 3 columns]

In [50]: dfrm.drop(dfrm.index[len(dfrm)-1])
Out[50]: 
          A         B         C
0  0.120064  0.785538  0.465853
1  0.431655  0.436866  0.640136
2  0.445904  0.311565  0.934073
3  0.981609  0.695210  0.911697
4  0.008632  0.629269  0.226454
5  0.577577  0.467475  0.510031
6  0.580909  0.232846  0.271254
7  0.696596  0.362825  0.556433
8  0.738912  0.932779  0.029723

[9 rows x 3 columns]

However, it’s much simpler to just write DF[:-1].

Surprised nobody brought this one up:

# To remove last n rows
df.head(-n)

# To remove first n rows
df.tail(-n)

Running a speed test on a DataFrame of 1000 rows shows that slicing and head/tail are ~6 times faster than using drop:

>>> %timeit df[:-1]
125 µs ± 132 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

>>> %timeit df.head(-1)
129 µs ± 1.18 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

>>> %timeit df.drop(df.tail(1).index)
751 µs ± 20.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

stats = pd.read_csv("C:\\py\\programs\\second pandas\\ex.csv")

The Output of stats:

       A            B          C
0   0.120064    0.785538    0.465853
1   0.431655    0.436866    0.640136
2   0.445904    0.311565    0.934073
3   0.981609    0.695210    0.911697
4   0.008632    0.629269    0.226454
5   0.577577    0.467475    0.510031
6   0.580909    0.232846    0.271254
7   0.696596    0.362825    0.556433
8   0.738912    0.932779    0.029723
9   0.834706    0.002989    0.333436

just use skipfooter=1

skipfooter : int, default 0

Number of lines at bottom of file to skip

stats_2 = pd.read_csv("C:\\py\\programs\\second pandas\\ex.csv", skipfooter=1, engine='python')

Output of stats_2

       A          B            C
0   0.120064    0.785538    0.465853
1   0.431655    0.436866    0.640136
2   0.445904    0.311565    0.934073
3   0.981609    0.695210    0.911697
4   0.008632    0.629269    0.226454
5   0.577577    0.467475    0.510031
6   0.580909    0.232846    0.271254
7   0.696596    0.362825    0.556433
8   0.738912    0.932779    0.029723

drop returns a new array so that is why it choked in the og post; I had a similar requirement to rename some column headers and deleted some rows due to an ill formed csv file converted to Dataframe, so after reading this post I used:

newList = pd.DataFrame(newList)
newList.columns = ['Area', 'Price']
print(newList)
# newList = newList.drop(0)
# newList = newList.drop(len(newList))
newList = newList[1:-1]
print(newList)

and it worked great, as you can see with the two commented out lines above I tried the drop.() method and it work but not as kool and readable as using [n:-n], hope that helps someone, thanks.

For more complex DataFrames that have a Multi-Index (say “Stock” and “Date”) and one wants to remove the last row for each Stock not just the last row of the last Stock, then the solution reads:

# To remove last n rows
df = df.groupby(level='Stock').apply(lambda x: x.head(-1)).reset_index(0, drop=True)

# To remove first n rows
df = df.groupby(level='Stock').apply(lambda x: x.tail(-1)).reset_index(0, drop=True)

As the groupby() is adding an additional level to the Multi-Index we just drop it at the end using reset_index(). The resulting df keeps the same type of Multi-Index as before the operation.