Reputation: 39
So I have a large dataframe, using pandas.
When I do max(df['A'])
it reports a max of 9999
when it should be 396450
by observation.
import numpy as numpy
import pandas as pd
f = open("20170901.as-rel2.txt", 'r')
#read file into array, ignore first 6 lines
lines = loadtxt("20170901.as-rel2.txt", dtype='str', comments="#", delimiter="|", unpack=False)
#ignore col 4
lines=lines[:, :3]
#convert to dataframe
df = pd.DataFrame(lines, columns=['A', 'B', 'C'])
After finding the max I have to count each node(col 'A')
and say how many times it is repeated.
Here is a sample of the file:
df=
A B C
0 2 45714 0
1 2 52685 -1
2 3 293 0
3 3 23248 -1
4 3 133296 0
5 3 265301 -1
6 5 28599 -1
7 5 52352 0
8 5 262879 -1
9 5 265048 -1
10 5 265316 -1
11 10 46392 0
.....
384338 396238 62605 -1
384339 396371 3785 -1
384340 396434 35039 -1
384341 396450 2495 -1
384342 396450 5078 -1
Expect:
[1, 0
2, 2
3, 4
4, 0
5, 5
10, 1
....]
I was going to run a for
loop of i <= maxvalue
(the maxvalue exceeds the number of rows).
and use counter. What is the the most effective method?
Upvotes: 2
Views: 451
Reputation: 294258
np.bincount
pd.Series(np.bincount(df.A))
0 0
1 0
2 2
3 4
4 0
5 5
6 0
7 0
8 0
9 0
10 1
dtype: int64
Upvotes: 2
Reputation: 323226
Using Categorical
with value_counts
df.A=pd.Categorical(df.A,categories=np.arange(1,max(df.A)+1))
df.A.value_counts().sort_index()
Out[312]:
1 0
2 2
3 4
4 0
5 5
6 0
7 0
8 0
9 0
Name: A, dtype: int64
Upvotes: 2