efficient way to fill a series of zeros with values at specific positions
Question:
I would like to find an efficient way to fill a series of zeros with values at specific positions, based on the index. Here is some exemplary code:
import pandas as pd
dict_0 = {x: 0 for x in range(1, 11)}
ser_0 = pd.Series(dict_0)
dict_vals={2:0.1, 5:0.9}
ser_vals=pd.Series(dict_vals)
ser_composed = pd.concat([ser_0[~ser_0.index.isin(ser_vals.index)],
ser_vals[ser_vals.index.isin(ser_vals.index)]])
ser_composed.sort_index()
As I want to apply it to much data, I’d like to find a way to do it more efficiently.
Can anyone help me?
Thanks a lot in advance!
Answers:
pd.Series.update is exactly what you’re looking for.
Note: It always modifies the Series in place. If you don’t want this, make a copy beforehand.
ser_0_original = ser_0.copy()
ser_0.update(ser_vals)
Just in case you’re creating a dataframe with zeros first to be filled afterwards, consider directly creating the series based on dict_vals, along the lines of
pd.Series(dict_vals, index=range(1,11)).fillna(0)
You can also use broadcasting
ser_0[dict_vals.keys()] = list(dict_vals.values())
This will modify ser_0, if you want to preserve it use a copy
ser_composed = ser_0.copy()
ser_composed[dict_vals.keys()] = list(dict_vals.values())
Output
1 0.0
2 0.1
3 0.0
4 0.0
5 0.9
6 0.0
7 0.0
8 0.0
9 0.0
10 0.0
I would like to find an efficient way to fill a series of zeros with values at specific positions, based on the index. Here is some exemplary code:
import pandas as pd
dict_0 = {x: 0 for x in range(1, 11)}
ser_0 = pd.Series(dict_0)
dict_vals={2:0.1, 5:0.9}
ser_vals=pd.Series(dict_vals)
ser_composed = pd.concat([ser_0[~ser_0.index.isin(ser_vals.index)],
ser_vals[ser_vals.index.isin(ser_vals.index)]])
ser_composed.sort_index()
As I want to apply it to much data, I’d like to find a way to do it more efficiently.
Can anyone help me?
Thanks a lot in advance!
pd.Series.update is exactly what you’re looking for.
Note: It always modifies the Series in place. If you don’t want this, make a copy beforehand.
ser_0_original = ser_0.copy()
ser_0.update(ser_vals)
Just in case you’re creating a dataframe with zeros first to be filled afterwards, consider directly creating the series based on dict_vals, along the lines of
pd.Series(dict_vals, index=range(1,11)).fillna(0)
You can also use broadcasting
ser_0[dict_vals.keys()] = list(dict_vals.values())
This will modify ser_0, if you want to preserve it use a copy
ser_composed = ser_0.copy()
ser_composed[dict_vals.keys()] = list(dict_vals.values())
Output
1 0.0
2 0.1
3 0.0
4 0.0
5 0.9
6 0.0
7 0.0
8 0.0
9 0.0
10 0.0