Dropping Nans From Selected Data In Pandas
Continuing on my previous question link (things are explained there), I now have obtained an array. However, I don't know how to use this array, but that is a further question. The
Solution 1:
Try
import pandas as pd
df = pd.read_csv("~/Truncated raw data hcl.csv")
data1 = df.iloc[:, [0, 1]]
cleaned_data = data1.dropna()
You were probably getting an Exception like "List does not have a method 'dropna'". That's because your data1 was not a Pandas DataFrame, but a List - and inside that list was a DataFrame.
Solution 2:
However the answer is already given, Though i would like to put some thoughts across this.
Importing Your dataFrame taking the example dataset from your earlier post you provided:
>>>import pandas as pd>>>df = pd.read_csv("so.csv")>>>df
time 1mnaoh trial 1 1mnaoh trial 2 1mnaoh trial 3 ... 5mnaoh trial 1 5mnaoh trial 2 5mnaoh trial 3 5mnaoh trial 4
0 0.0 23.2 23.1 23.1 ... 23.3 24.3 24.1 24.1
1 0.5 23.2 23.1 23.1 ... 23.4 24.3 24.1 24.1
2 1.0 23.2 23.1 23.1 ... 23.5 24.3 24.1 24.1
3 1.5 23.2 23.1 23.1 ... 23.6 24.3 24.1 24.1
4 2.0 23.3 23.2 23.2 ... 23.7 24.5 24.7 25.1
5 2.5 24.0 23.5 23.5 ... 23.8 27.2 26.7 28.1
6 3.0 25.4 24.4 24.1 ... 23.9 31.4 29.8 31.3
7 3.5 26.9 25.5 25.1 ... 23.9 35.1 33.2 34.4
8 4.0 27.8 26.5 26.2 ... 24.0 37.7 35.9 36.8
9 4.5 28.5 27.3 27.0 ... 24.0 39.7 38.0 38.7
10 5.0 28.9 27.9 27.7 ... 24.0 40.9 39.6 40.2
11 5.5 29.2 28.2 28.3 ... 24.0 41.9 40.7 41.0
12 6.0 29.4 28.5 28.6 ... 24.1 42.5 41.6 41.2
13 6.5 29.5 28.8 28.9 ... 24.1 43.1 42.3 41.7
14 7.0 29.6 29.0 29.1 ... 24.1 43.4 42.8 42.3
15 7.5 29.7 29.2 29.2 ... 24.0 43.7 43.1 42.9
16 8.0 29.8 29.3 29.3 ... 24.2 43.8 43.3 43.3
17 8.5 29.8 29.4 29.4 ... 27.0 43.9 43.5 43.6
18 9.0 29.9 29.5 29.5 ... 30.8 44.0 43.6 43.8
19 9.5 29.9 29.6 29.5 ... 33.9 44.0 43.7 44.0
20 10.0 30.0 29.7 29.6 ... 36.2 44.0 43.7 44.1
21 10.5 30.0 29.7 29.6 ... 37.9 44.0 43.8 44.2
22 11.0 30.0 29.7 29.6 ... 39.3 NaN 43.8 44.3
23 11.5 30.0 29.8 29.7 ... 40.2 NaN 43.8 44.3
24 12.0 30.0 29.8 29.7 ... 40.9 NaN 43.9 44.3
25 12.5 30.1 29.8 29.7 ... 41.4 NaN 43.9 44.3
26 13.0 30.1 29.8 29.8 ... 41.8 NaN 43.9 44.4
27 13.5 30.1 29.9 29.8 ... 42.0 NaN 43.9 44.4
28 14.0 30.1 29.9 29.8 ... 42.1 NaN NaN 44.4
29 14.5 NaN 29.9 29.8 ... 42.3 NaN NaN 44.4
30 15.0 NaN 29.9 NaN ... 42.4 NaN NaN NaN
31 15.5 NaN NaN NaN ... 42.4 NaN NaN NaN
However, It good to clean the data beforehand and then process the data as you desired hence dropping the NA values during import itself will be significantly useful.
>>>df = pd.read_csv("so.csv").dropna() <-- dropping the NA here itself>>>df
time 1mnaoh trial 1 1mnaoh trial 2 1mnaoh trial 3 ... 5mnaoh trial 1 5mnaoh trial 2 5mnaoh trial 3 5mnaoh trial 4
0 0.0 23.2 23.1 23.1 ... 23.3 24.3 24.1 24.1
1 0.5 23.2 23.1 23.1 ... 23.4 24.3 24.1 24.1
2 1.0 23.2 23.1 23.1 ... 23.5 24.3 24.1 24.1
3 1.5 23.2 23.1 23.1 ... 23.6 24.3 24.1 24.1
4 2.0 23.3 23.2 23.2 ... 23.7 24.5 24.7 25.1
5 2.5 24.0 23.5 23.5 ... 23.8 27.2 26.7 28.1
6 3.0 25.4 24.4 24.1 ... 23.9 31.4 29.8 31.3
7 3.5 26.9 25.5 25.1 ... 23.9 35.1 33.2 34.4
8 4.0 27.8 26.5 26.2 ... 24.0 37.7 35.9 36.8
9 4.5 28.5 27.3 27.0 ... 24.0 39.7 38.0 38.7
10 5.0 28.9 27.9 27.7 ... 24.0 40.9 39.6 40.2
11 5.5 29.2 28.2 28.3 ... 24.0 41.9 40.7 41.0
12 6.0 29.4 28.5 28.6 ... 24.1 42.5 41.6 41.2
13 6.5 29.5 28.8 28.9 ... 24.1 43.1 42.3 41.7
14 7.0 29.6 29.0 29.1 ... 24.1 43.4 42.8 42.3
15 7.5 29.7 29.2 29.2 ... 24.0 43.7 43.1 42.9
16 8.0 29.8 29.3 29.3 ... 24.2 43.8 43.3 43.3
17 8.5 29.8 29.4 29.4 ... 27.0 43.9 43.5 43.6
18 9.0 29.9 29.5 29.5 ... 30.8 44.0 43.6 43.8
19 9.5 29.9 29.6 29.5 ... 33.9 44.0 43.7 44.0
20 10.0 30.0 29.7 29.6 ... 36.2 44.0 43.7 44.1
21 10.5 30.0 29.7 29.6 ... 37.9 44.0 43.8 44.2
and lastly cast your dataFrame as you wish:
>>> df = [df.iloc[:, [0, 1]]]
# new_df = [df.iloc[:, [0, 1]]] <-- if you don't want to alter actual dataFrame
>>> df
[ time 1mnaoh trial 1
0 0.0 23.2
1 0.5 23.2
2 1.0 23.2
3 1.5 23.2
4 2.0 23.3
5 2.5 24.0
6 3.0 25.4
7 3.5 26.9
8 4.0 27.8
9 4.5 28.5
10 5.0 28.9
11 5.5 29.2
12 6.0 29.4
13 6.5 29.5
14 7.0 29.6
15 7.5 29.7
16 8.0 29.8
17 8.5 29.8
18 9.0 29.9
19 9.5 29.9
20 10.0 30.0
21 10.5 30.0]
Better Answer :
While looking at the end result, i see you are just concerning about the particular columns those are 'time' & '1mnaoh trial 1' hence idealistic would be to use usecole option which will reduce your memory footprint for the search across the data because you just opted the only columns which are useful for you and then use dropna() which will give you wanted you wanted i believe.
>>>df = pd.read_csv("so.csv", usecols=['time', '1mnaoh trial 1']).dropna()>>>df
time 1mnaoh trial 1
0 0.0 23.2
1 0.5 23.2
2 1.0 23.2
3 1.5 23.2
4 2.0 23.3
5 2.5 24.0
6 3.0 25.4
7 3.5 26.9
8 4.0 27.8
9 4.5 28.5
10 5.0 28.9
11 5.5 29.2
12 6.0 29.4
13 6.5 29.5
14 7.0 29.6
15 7.5 29.7
16 8.0 29.8
17 8.5 29.8
18 9.0 29.9
19 9.5 29.9
20 10.0 30.0
21 10.5 30.0
22 11.0 30.0
23 11.5 30.0
24 12.0 30.0
25 12.5 30.1
26 13.0 30.1
27 13.5 30.1
28 14.0 30.1
Post a Comment for "Dropping Nans From Selected Data In Pandas"