Pandas Group by Values and Merge Rows Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern) Data science time! April 2019 and salary with experience Should we burninate the [wrap] tag? The Ask Question Wizard is Live!How to merge two dictionaries in a single expression?How do I sort a dictionary by value?Add one row to pandas DataFrameRenaming columns in pandasDelete column from pandas DataFrame by column nameHow to drop rows of Pandas DataFrame whose value in certain columns is NaNHow to iterate over rows in a DataFrame in Pandas?Select rows from a DataFrame based on values in a column in pandasPandas: Reassigning values in dataframeMerge rows in DataFrame by removing nan's after groupby
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Pandas Group by Values and Merge Rows
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern)
Data science time! April 2019 and salary with experience
Should we burninate the [wrap] tag?
The Ask Question Wizard is Live!How to merge two dictionaries in a single expression?How do I sort a dictionary by value?Add one row to pandas DataFrameRenaming columns in pandasDelete column from pandas DataFrame by column nameHow to drop rows of Pandas DataFrame whose value in certain columns is NaNHow to iterate over rows in a DataFrame in Pandas?Select rows from a DataFrame based on values in a column in pandasPandas: Reassigning values in dataframeMerge rows in DataFrame by removing nan's after groupby
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I have a DataFrame and I want to merge the rows that contain same values
toy = [
[10, 11],
[21, 22],
[11, 15],
[22, 23],
[15, 33]
]
toy = pd.DataFrame(toy, columns = ['ID1', 'ID2'])
ID1 ID2
0 10 11
1 21 22
2 11 15
3 22 23
4 15 33
What I am hoping to get afterwards is
0 1 2 3
0 10 11 15 33.0
1 21 22 23 NaN
So merging rows that contain any same value within.
My solution is super NOT elegant, I am seeking for the right way to do this... Recursion? Groupby? Hmm..
#### Feel Free to NOT read this... ###
for k in range(100):
print(k)
merge_df = []
merged_indices = []
for i, row in toy.iterrows():
if i in merged_indices:
continue
cp = toy.copy()
merge_rows = cp[cp.isin(row.values)].dropna(how="all")
merged_indices = merged_indices + list(merge_rows.index)
merge_rows = np.array(toy.iloc[merge_rows.index]).flatten()
merge_rows = np.unique(merge_rows)
merge_df.append(merge_rows)
if toy.shape[0] == len(merge_df):
break
toy = pd.DataFrame(merge_df).copy()
python pandas
asked Mar 8 at 16:34
LYuLYu
1,32711230
add a comment |
I have a DataFrame and I want to merge the rows that contain same values
toy = [
[10, 11],
[21, 22],
[11, 15],
[22, 23],
[15, 33]
]
toy = pd.DataFrame(toy, columns = ['ID1', 'ID2'])
ID1 ID2
0 10 11
1 21 22
2 11 15
3 22 23
4 15 33
What I am hoping to get afterwards is
0 1 2 3
0 10 11 15 33.0
1 21 22 23 NaN
So merging rows that contain any same value within.
My solution is super NOT elegant, I am seeking for the right way to do this... Recursion? Groupby? Hmm..
#### Feel Free to NOT read this... ###
for k in range(100):
print(k)
merge_df = []
merged_indices = []
for i, row in toy.iterrows():
if i in merged_indices:
continue
cp = toy.copy()
merge_rows = cp[cp.isin(row.values)].dropna(how="all")
merged_indices = merged_indices + list(merge_rows.index)
merge_rows = np.array(toy.iloc[merge_rows.index]).flatten()
merge_rows = np.unique(merge_rows)
merge_df.append(merge_rows)
if toy.shape[0] == len(merge_df):
break
toy = pd.DataFrame(merge_df).copy()
python pandas
asked Mar 8 at 16:34
LYuLYu
1,32711230
1
BTW nice pic of invoker :-)
– Wen-Ben
Mar 8 at 17:32
@Wen-Ben haha - thx, I like your Saitama too :-)
– LYu
Mar 8 at 17:38
add a comment |
I have a DataFrame and I want to merge the rows that contain same values
toy = [
[10, 11],
[21, 22],
[11, 15],
[22, 23],
[15, 33]
]
toy = pd.DataFrame(toy, columns = ['ID1', 'ID2'])
ID1 ID2
0 10 11
1 21 22
2 11 15
3 22 23
4 15 33
What I am hoping to get afterwards is
0 1 2 3
0 10 11 15 33.0
1 21 22 23 NaN
So merging rows that contain any same value within.
My solution is super NOT elegant, I am seeking for the right way to do this... Recursion? Groupby? Hmm..
#### Feel Free to NOT read this... ###
for k in range(100):
print(k)
merge_df = []
merged_indices = []
for i, row in toy.iterrows():
if i in merged_indices:
continue
cp = toy.copy()
merge_rows = cp[cp.isin(row.values)].dropna(how="all")
merged_indices = merged_indices + list(merge_rows.index)
merge_rows = np.array(toy.iloc[merge_rows.index]).flatten()
merge_rows = np.unique(merge_rows)
merge_df.append(merge_rows)
if toy.shape[0] == len(merge_df):
break
toy = pd.DataFrame(merge_df).copy()
python pandas
asked Mar 8 at 16:34
LYuLYu
1,32711230
I have a DataFrame and I want to merge the rows that contain same values
toy = [
[10, 11],
[21, 22],
[11, 15],
[22, 23],
[15, 33]
]
toy = pd.DataFrame(toy, columns = ['ID1', 'ID2'])
ID1 ID2
0 10 11
1 21 22
2 11 15
3 22 23
4 15 33
What I am hoping to get afterwards is
0 1 2 3
0 10 11 15 33.0
1 21 22 23 NaN
So merging rows that contain any same value within.
My solution is super NOT elegant, I am seeking for the right way to do this... Recursion? Groupby? Hmm..
#### Feel Free to NOT read this... ###
for k in range(100):
print(k)
merge_df = []
merged_indices = []
for i, row in toy.iterrows():
if i in merged_indices:
continue
cp = toy.copy()
merge_rows = cp[cp.isin(row.values)].dropna(how="all")
merged_indices = merged_indices + list(merge_rows.index)
merge_rows = np.array(toy.iloc[merge_rows.index]).flatten()
merge_rows = np.unique(merge_rows)
merge_df.append(merge_rows)
if toy.shape[0] == len(merge_df):
break
toy = pd.DataFrame(merge_df).copy()
python pandas
python pandas
asked Mar 8 at 16:34
LYuLYu
1,32711230
asked Mar 8 at 16:34
LYuLYu
1,32711230
asked Mar 8 at 16:34
LYuLYu
1,32711230
asked Mar 8 at 16:34
LYuLYu
1,32711230
asked Mar 8 at 16:34
LYuLYu
1,32711230
1,32711230
1
BTW nice pic of invoker :-)
– Wen-Ben
Mar 8 at 17:32
@Wen-Ben haha - thx, I like your Saitama too :-)
– LYu
Mar 8 at 17:38
add a comment |
1
BTW nice pic of invoker :-)
– Wen-Ben
Mar 8 at 17:32
@Wen-Ben haha - thx, I like your Saitama too :-)
– LYu
Mar 8 at 17:38
1
1
BTW nice pic of invoker :-)
– Wen-Ben
Mar 8 at 17:32
BTW nice pic of invoker :-)
– Wen-Ben
Mar 8 at 17:32
@Wen-Ben haha - thx, I like your Saitama too :-)
– LYu
Mar 8 at 17:38
@Wen-Ben haha - thx, I like your Saitama too :-)
– LYu
Mar 8 at 17:38
add a comment |
1 Answer
1
active
oldest
votes
Sounds like a network problems so I using networkx
import networkx as nx
G=nx.from_pandas_edgelist(toy, 'ID1', 'ID2')
l=list(nx.connected_components(G))
newdf=pd.DataFrame(l)
newdf
Out[896]:
0 1 2 3
0 33 10 11 15.0
1 21 22 23 NaN
answered Mar 8 at 17:31
Wen-BenWen-Ben
126k83872
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
Sounds like a network problems so I using networkx
import networkx as nx
G=nx.from_pandas_edgelist(toy, 'ID1', 'ID2')
l=list(nx.connected_components(G))
newdf=pd.DataFrame(l)
newdf
Out[896]:
0 1 2 3
0 33 10 11 15.0
1 21 22 23 NaN
answered Mar 8 at 17:31
Wen-BenWen-Ben
126k83872
add a comment |
Sounds like a network problems so I using networkx
import networkx as nx
G=nx.from_pandas_edgelist(toy, 'ID1', 'ID2')
l=list(nx.connected_components(G))
newdf=pd.DataFrame(l)
newdf
Out[896]:
0 1 2 3
0 33 10 11 15.0
1 21 22 23 NaN
answered Mar 8 at 17:31
Wen-BenWen-Ben
126k83872
add a comment |
Sounds like a network problems so I using networkx
import networkx as nx
G=nx.from_pandas_edgelist(toy, 'ID1', 'ID2')
l=list(nx.connected_components(G))
newdf=pd.DataFrame(l)
newdf
Out[896]:
0 1 2 3
0 33 10 11 15.0
1 21 22 23 NaN
answered Mar 8 at 17:31
Wen-BenWen-Ben
126k83872
Sounds like a network problems so I using networkx
import networkx as nx
G=nx.from_pandas_edgelist(toy, 'ID1', 'ID2')
l=list(nx.connected_components(G))
newdf=pd.DataFrame(l)
newdf
Out[896]:
0 1 2 3
0 33 10 11 15.0
1 21 22 23 NaN
answered Mar 8 at 17:31
Wen-BenWen-Ben
126k83872
answered Mar 8 at 17:31
Wen-BenWen-Ben
126k83872
answered Mar 8 at 17:31
Wen-BenWen-Ben
126k83872
answered Mar 8 at 17:31
Wen-BenWen-Ben
126k83872
126k83872
add a comment |
add a comment |
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1
BTW nice pic of invoker :-)
– Wen-Ben
Mar 8 at 17:32
@Wen-Ben haha - thx, I like your Saitama too :-)
– LYu
Mar 8 at 17:38