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How to update a DataFrame with a another DataFrame by its index?
How do I expand the output display to see more columns?Adding new column to existing DataFrame in Python pandasDelete column from pandas DataFrameHow to iterate over rows in a DataFrame in Pandas?Select rows from a DataFrame based on values in a column in pandasCreate an empty data frame with index from another data framegrouping rows in list in pandas groupbyhow to sort pandas dataframe from one columnCopying 1 line from a panda dataframe into multiple lines of anotherHow to pivot a dataframe
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty height:90px;width:728px;box-sizing:border-box;
I have a origin data frame as below:
0 0
1 6.19
2 6.19
3 16.19
4 16.19
5 179.33
6 179.33
7 179.33
8 179.33
9 179.33
10 179.33
11 0
12 179.33
13 179.33
14 0
15 0
16 0
17 0
18 0
19 11.49
20 11.49
21 7.15
22 7.15
23 16.19
24 16.19
25 17.85
26 17.85
And the second Data Frame is:
2 3.19
4 16.19
6 179.33
8 179.33
10 179.33
13 179.33
20 11.49
22 7.15
24 16.19
26 17.85
You can see the first column is index, and I want it to be updated according the second data list.
For example:
0 0
1 6.19
2 6.19
Since my second data Frame is 3.19 with index at 2. so my expect output should be like:
0 0
1 6.19
2 3.19
How to I reach that? BTW, I have try to do that like:
for i in df.index:
new = df2.aa[i]
if new:
df.loc['aa'][i]=new
But it should have a good way to do that in pandas, plz help me.
python-3.x pandas
asked Mar 25 at 7:38
Frank AKFrank AK
1,182921
add a comment |
I have a origin data frame as below:
0 0
1 6.19
2 6.19
3 16.19
4 16.19
5 179.33
6 179.33
7 179.33
8 179.33
9 179.33
10 179.33
11 0
12 179.33
13 179.33
14 0
15 0
16 0
17 0
18 0
19 11.49
20 11.49
21 7.15
22 7.15
23 16.19
24 16.19
25 17.85
26 17.85
And the second Data Frame is:
2 3.19
4 16.19
6 179.33
8 179.33
10 179.33
13 179.33
20 11.49
22 7.15
24 16.19
26 17.85
You can see the first column is index, and I want it to be updated according the second data list.
For example:
0 0
1 6.19
2 6.19
Since my second data Frame is 3.19 with index at 2. so my expect output should be like:
0 0
1 6.19
2 3.19
How to I reach that? BTW, I have try to do that like:
for i in df.index:
new = df2.aa[i]
if new:
df.loc['aa'][i]=new
But it should have a good way to do that in pandas, plz help me.
python-3.x pandas
asked Mar 25 at 7:38
Frank AKFrank AK
1,182921
add a comment |
I have a origin data frame as below:
0 0
1 6.19
2 6.19
3 16.19
4 16.19
5 179.33
6 179.33
7 179.33
8 179.33
9 179.33
10 179.33
11 0
12 179.33
13 179.33
14 0
15 0
16 0
17 0
18 0
19 11.49
20 11.49
21 7.15
22 7.15
23 16.19
24 16.19
25 17.85
26 17.85
And the second Data Frame is:
2 3.19
4 16.19
6 179.33
8 179.33
10 179.33
13 179.33
20 11.49
22 7.15
24 16.19
26 17.85
You can see the first column is index, and I want it to be updated according the second data list.
For example:
0 0
1 6.19
2 6.19
Since my second data Frame is 3.19 with index at 2. so my expect output should be like:
0 0
1 6.19
2 3.19
How to I reach that? BTW, I have try to do that like:
for i in df.index:
new = df2.aa[i]
if new:
df.loc['aa'][i]=new
But it should have a good way to do that in pandas, plz help me.
python-3.x pandas
asked Mar 25 at 7:38
Frank AKFrank AK
1,182921
I have a origin data frame as below:
0 0
1 6.19
2 6.19
3 16.19
4 16.19
5 179.33
6 179.33
7 179.33
8 179.33
9 179.33
10 179.33
11 0
12 179.33
13 179.33
14 0
15 0
16 0
17 0
18 0
19 11.49
20 11.49
21 7.15
22 7.15
23 16.19
24 16.19
25 17.85
26 17.85
And the second Data Frame is:
2 3.19
4 16.19
6 179.33
8 179.33
10 179.33
13 179.33
20 11.49
22 7.15
24 16.19
26 17.85
You can see the first column is index, and I want it to be updated according the second data list.
For example:
0 0
1 6.19
2 6.19
Since my second data Frame is 3.19 with index at 2. so my expect output should be like:
0 0
1 6.19
2 3.19
How to I reach that? BTW, I have try to do that like:
for i in df.index:
new = df2.aa[i]
if new:
df.loc['aa'][i]=new
But it should have a good way to do that in pandas, plz help me.
python-3.x pandas
python-3.x pandas
asked Mar 25 at 7:38
Frank AKFrank AK
1,182921
asked Mar 25 at 7:38
Frank AKFrank AK
1,182921
edited Mar 25 at 8:44
Frank AK
asked Mar 25 at 7:38
Frank AKFrank AK
1,182921
asked Mar 25 at 7:38
Frank AKFrank AK
1,182921
asked Mar 25 at 7:38
Frank AKFrank AK
1,182921
1,182921
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
Use Series.combine_first or Series.update:
df1['col'] = df2['col'].combine_first(df1['col'])
Or:
df1['col'].update(df2['col'])
print (df1)
col
0 0.00
1 6.19
2 3.19
3 16.19
4 16.19
5 179.33
6 179.33
7 179.33
8 179.33
9 179.33
10 179.33
11 0.00
12 179.33
13 179.33
14 0.00
15 0.00
16 0.00
17 0.00
18 0.00
19 11.49
20 11.49
21 7.15
22 7.15
23 16.19
24 16.19
25 17.85
26 17.85
answered Mar 25 at 7:41
jezraeljezrael
381k27373445
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Use Series.combine_first or Series.update:
df1['col'] = df2['col'].combine_first(df1['col'])
Or:
df1['col'].update(df2['col'])
print (df1)
col
0 0.00
1 6.19
2 3.19
3 16.19
4 16.19
5 179.33
6 179.33
7 179.33
8 179.33
9 179.33
10 179.33
11 0.00
12 179.33
13 179.33
14 0.00
15 0.00
16 0.00
17 0.00
18 0.00
19 11.49
20 11.49
21 7.15
22 7.15
23 16.19
24 16.19
25 17.85
26 17.85
answered Mar 25 at 7:41
jezraeljezrael
381k27373445
add a comment |
Use Series.combine_first or Series.update:
df1['col'] = df2['col'].combine_first(df1['col'])
Or:
df1['col'].update(df2['col'])
print (df1)
col
0 0.00
1 6.19
2 3.19
3 16.19
4 16.19
5 179.33
6 179.33
7 179.33
8 179.33
9 179.33
10 179.33
11 0.00
12 179.33
13 179.33
14 0.00
15 0.00
16 0.00
17 0.00
18 0.00
19 11.49
20 11.49
21 7.15
22 7.15
23 16.19
24 16.19
25 17.85
26 17.85
answered Mar 25 at 7:41
jezraeljezrael
381k27373445
add a comment |
Use Series.combine_first or Series.update:
df1['col'] = df2['col'].combine_first(df1['col'])
Or:
df1['col'].update(df2['col'])
print (df1)
col
0 0.00
1 6.19
2 3.19
3 16.19
4 16.19
5 179.33
6 179.33
7 179.33
8 179.33
9 179.33
10 179.33
11 0.00
12 179.33
13 179.33
14 0.00
15 0.00
16 0.00
17 0.00
18 0.00
19 11.49
20 11.49
21 7.15
22 7.15
23 16.19
24 16.19
25 17.85
26 17.85
answered Mar 25 at 7:41
jezraeljezrael
381k27373445
Use Series.combine_first or Series.update:
df1['col'] = df2['col'].combine_first(df1['col'])
Or:
df1['col'].update(df2['col'])
print (df1)
col
0 0.00
1 6.19
2 3.19
3 16.19
4 16.19
5 179.33
6 179.33
7 179.33
8 179.33
9 179.33
10 179.33
11 0.00
12 179.33
13 179.33
14 0.00
15 0.00
16 0.00
17 0.00
18 0.00
19 11.49
20 11.49
21 7.15
22 7.15
23 16.19
24 16.19
25 17.85
26 17.85
answered Mar 25 at 7:41
jezraeljezrael
381k27373445
edited Mar 25 at 7:48
answered Mar 25 at 7:41
jezraeljezrael
381k27373445
answered Mar 25 at 7:41
jezraeljezrael
381k27373445
answered Mar 25 at 7:41
jezraeljezrael
381k27373445
381k27373445
add a comment |
add a comment |
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