The following is the pandas dataframe I have:
cluster Value
1 A
1 NaN
1 NaN
1 NaN
1 NaN
2 NaN
2 NaN
2 B
2 NaN
3 NaN
3 NaN
3 C
3 NaN
4 NaN
4 S
4 NaN
5 NaN
5 A
5 NaN
5 NaN
If we look into the data, cluster 1 has Value 'A' for one row and remain all are NA values. I want to fill 'A' value for all the rows of cluster 1. Similarly for all the clusters. Based on one of the values of the cluster, I want to fill the remaining rows of the cluster. The output should be like,
cluster Value
1 A
1 A
1 A
1 A
1 A
2 B
2 B
2 B
2 B
3 C
3 C
3 C
3 C
4 S
4 S
4 S
5 A
5 A
5 A
5 A
I am new to python and not sure how to proceed with this. Can anybody help with this ?
groupby
+ bfill
, and ffill
df = df.groupby('cluster').bfill().ffill()
df
cluster Value
0 1 A
1 1 A
2 1 A
3 1 A
4 1 A
5 2 B
6 2 B
7 2 B
8 2 B
9 3 B
10 3 B
11 3 C
12 3 C
13 4 S
14 4 S
15 4 S
16 5 A
17 5 A
18 5 A
19 5 A
Or,
groupby
+ transform
with first
df['Value'] = df.groupby('cluster').Value.transform('first')
df
cluster Value
0 1 A
1 1 A
2 1 A
3 1 A
4 1 A
5 2 B
6 2 B
7 2 B
8 2 B
9 3 B
10 3 B
11 3 C
12 3 C
13 4 S
14 4 S
15 4 S
16 5 A
17 5 A
18 5 A
19 5 A
Edit
The following seems better:
nan_map = df.dropna().set_index('cluster').to_dict()['Value']
df['Value'] = df['cluster'].map(nan_map)
print(df)
Original
I can't think of a better way to do this than iterate over all the rows, but one might exist. First I built your DataFrame:
import pandas as pd
import math
# Build your DataFrame
df = pd.DataFrame.from_items([
('cluster', [1,1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,5,5,5,5]),
('Value', [float('nan') for _ in range(20)]),
])
df['Value'] = df['Value'].astype(object)
df.at[ 0,'Value'] = 'A'
df.at[ 7,'Value'] = 'B'
df.at[11,'Value'] = 'C'
df.at[14,'Value'] = 'S'
df.at[17,'Value'] = 'A'
Now here's an approach that first creates a nan_map
dict, then sets the values in Value
as specified in the dict.
# Create a dict to map clusters to unique values
nan_map = df.dropna().set_index('cluster').to_dict()['Value']
# nan_map: {1: 'A', 2: 'B', 3: 'C', 4: 'S', 5: 'A'}
# Apply
for i, row in df.iterrows():
df.at[i,'Value'] = nan_map[row['cluster']]
print(df)
Output:
cluster Value 0 1 A 1 1 A 2 1 A 3 1 A 4 1 A 5 2 B 6 2 B 7 2 B 8 2 B 9 3 C 10 3 C 11 3 C 12 3 C 13 4 S 14 4 S 15 4 S 16 5 A 17 5 A 18 5 A 19 5 A
Note: This sets all values based on the cluster and doesn't check for NaN-ness. You may want to experiment with something like:
# Apply
for i, row in df.iterrows():
if isinstance(df.at[i,'Value'], float) and math.isnan(df.at[i,'Value']):
df.at[i,'Value'] = nan_map[row['cluster']]
to see which is more efficient (my guess is the former, without the checks).
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