Pandas: how to merge to dataframes on multiple columns?
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I have 2 dataframes, df1 and df2.
df1 Contains the information of some interactions between people.
df1
Name1 Name2
0 Jack John
1 Sarah Jack
2 Sarah Eva
3 Eva Tom
4 Eva John
df2 Contains the status of general people and also some people in df1
df2
Name Y
0 Jack 0
1 John 1
2 Sarah 0
3 Tom 1
4 Laura 0
I would like df2 only for the people that are in df1 (Laura disappears), and for those that are not in df2 keep NaN (i.e. Eva) such as:
df2
Name Y
0 Jack 0
1 John 1
2 Sarah 0
3 Tom 1
4 Eva NaN
python pandas dataframe
asked Nov 15 '18 at 11:18
emaxemax
1,22531235
add a comment |
I have 2 dataframes, df1 and df2.
df1 Contains the information of some interactions between people.
df1
Name1 Name2
0 Jack John
1 Sarah Jack
2 Sarah Eva
3 Eva Tom
4 Eva John
df2 Contains the status of general people and also some people in df1
df2
Name Y
0 Jack 0
1 John 1
2 Sarah 0
3 Tom 1
4 Laura 0
I would like df2 only for the people that are in df1 (Laura disappears), and for those that are not in df2 keep NaN (i.e. Eva) such as:
df2
Name Y
0 Jack 0
1 John 1
2 Sarah 0
3 Tom 1
4 Eva NaN
python pandas dataframe
asked Nov 15 '18 at 11:18
emaxemax
1,22531235
Please share your dfs asdf.to_dict()
– user32185
Nov 15 '18 at 11:51
add a comment |
I have 2 dataframes, df1 and df2.
df1 Contains the information of some interactions between people.
df1
Name1 Name2
0 Jack John
1 Sarah Jack
2 Sarah Eva
3 Eva Tom
4 Eva John
df2 Contains the status of general people and also some people in df1
df2
Name Y
0 Jack 0
1 John 1
2 Sarah 0
3 Tom 1
4 Laura 0
I would like df2 only for the people that are in df1 (Laura disappears), and for those that are not in df2 keep NaN (i.e. Eva) such as:
df2
Name Y
0 Jack 0
1 John 1
2 Sarah 0
3 Tom 1
4 Eva NaN
python pandas dataframe
asked Nov 15 '18 at 11:18
emaxemax
1,22531235
I have 2 dataframes, df1 and df2.
df1 Contains the information of some interactions between people.
df1
Name1 Name2
0 Jack John
1 Sarah Jack
2 Sarah Eva
3 Eva Tom
4 Eva John
df2 Contains the status of general people and also some people in df1
df2
Name Y
0 Jack 0
1 John 1
2 Sarah 0
3 Tom 1
4 Laura 0
I would like df2 only for the people that are in df1 (Laura disappears), and for those that are not in df2 keep NaN (i.e. Eva) such as:
df2
Name Y
0 Jack 0
1 John 1
2 Sarah 0
3 Tom 1
4 Eva NaN
python pandas dataframe
python pandas dataframe
asked Nov 15 '18 at 11:18
emaxemax
1,22531235
asked Nov 15 '18 at 11:18
emaxemax
1,22531235
asked Nov 15 '18 at 11:18
emaxemax
1,22531235
asked Nov 15 '18 at 11:18
emaxemax
1,22531235
asked Nov 15 '18 at 11:18
emaxemax
1,22531235
1,22531235
Please share your dfs asdf.to_dict()
– user32185
Nov 15 '18 at 11:51
add a comment |
Please share your dfs asdf.to_dict()
– user32185
Nov 15 '18 at 11:51
Please share your dfs as
df.to_dict()– user32185
Nov 15 '18 at 11:51
Please share your dfs as
df.to_dict()– user32185
Nov 15 '18 at 11:51
add a comment |
2 Answers
2
active
oldest
votes
Create a DataFrame on unique values of df1 and map it with df2 as:
df = pd.DataFrame(np.unique(df1.values),columns=['Name'])
df['Y'] = df.Name.map(df2.set_index('Name')['Y'])
print(df)
Name Y
0 Eva NaN
1 Jack 0.0
2 John 1.0
3 Sarah 0.0
4 Tom 1.0
Note : Order is not preserved.
answered Nov 15 '18 at 11:25
Sandeep KadapaSandeep Kadapa
7,408831
add a comment |
You can create a list of unique names in df1 and use isin
names = np.unique(df1[['Name1', 'Name2']].values.ravel())
df2.loc[~df2['Name'].isin(names), 'Y'] = np.nan
Name Y
0 Jack 0.0
1 John 1.0
2 Sarah 0.0
3 Tom 1.0
4 Laura NaN
answered Nov 15 '18 at 17:12
VaishaliVaishali
22.7k41438
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
Create a DataFrame on unique values of df1 and map it with df2 as:
df = pd.DataFrame(np.unique(df1.values),columns=['Name'])
df['Y'] = df.Name.map(df2.set_index('Name')['Y'])
print(df)
Name Y
0 Eva NaN
1 Jack 0.0
2 John 1.0
3 Sarah 0.0
4 Tom 1.0
Note : Order is not preserved.
answered Nov 15 '18 at 11:25
Sandeep KadapaSandeep Kadapa
7,408831
add a comment |
Create a DataFrame on unique values of df1 and map it with df2 as:
df = pd.DataFrame(np.unique(df1.values),columns=['Name'])
df['Y'] = df.Name.map(df2.set_index('Name')['Y'])
print(df)
Name Y
0 Eva NaN
1 Jack 0.0
2 John 1.0
3 Sarah 0.0
4 Tom 1.0
Note : Order is not preserved.
answered Nov 15 '18 at 11:25
Sandeep KadapaSandeep Kadapa
7,408831
add a comment |
Create a DataFrame on unique values of df1 and map it with df2 as:
df = pd.DataFrame(np.unique(df1.values),columns=['Name'])
df['Y'] = df.Name.map(df2.set_index('Name')['Y'])
print(df)
Name Y
0 Eva NaN
1 Jack 0.0
2 John 1.0
3 Sarah 0.0
4 Tom 1.0
Note : Order is not preserved.
answered Nov 15 '18 at 11:25
Sandeep KadapaSandeep Kadapa
7,408831
Create a DataFrame on unique values of df1 and map it with df2 as:
df = pd.DataFrame(np.unique(df1.values),columns=['Name'])
df['Y'] = df.Name.map(df2.set_index('Name')['Y'])
print(df)
Name Y
0 Eva NaN
1 Jack 0.0
2 John 1.0
3 Sarah 0.0
4 Tom 1.0
Note : Order is not preserved.
answered Nov 15 '18 at 11:25
Sandeep KadapaSandeep Kadapa
7,408831
answered Nov 15 '18 at 11:25
Sandeep KadapaSandeep Kadapa
7,408831
answered Nov 15 '18 at 11:25
Sandeep KadapaSandeep Kadapa
7,408831
answered Nov 15 '18 at 11:25
Sandeep KadapaSandeep Kadapa
7,408831
7,408831
add a comment |
add a comment |
You can create a list of unique names in df1 and use isin
names = np.unique(df1[['Name1', 'Name2']].values.ravel())
df2.loc[~df2['Name'].isin(names), 'Y'] = np.nan
Name Y
0 Jack 0.0
1 John 1.0
2 Sarah 0.0
3 Tom 1.0
4 Laura NaN
answered Nov 15 '18 at 17:12
VaishaliVaishali
22.7k41438
add a comment |
You can create a list of unique names in df1 and use isin
names = np.unique(df1[['Name1', 'Name2']].values.ravel())
df2.loc[~df2['Name'].isin(names), 'Y'] = np.nan
Name Y
0 Jack 0.0
1 John 1.0
2 Sarah 0.0
3 Tom 1.0
4 Laura NaN
answered Nov 15 '18 at 17:12
VaishaliVaishali
22.7k41438
add a comment |
You can create a list of unique names in df1 and use isin
names = np.unique(df1[['Name1', 'Name2']].values.ravel())
df2.loc[~df2['Name'].isin(names), 'Y'] = np.nan
Name Y
0 Jack 0.0
1 John 1.0
2 Sarah 0.0
3 Tom 1.0
4 Laura NaN
answered Nov 15 '18 at 17:12
VaishaliVaishali
22.7k41438
You can create a list of unique names in df1 and use isin
names = np.unique(df1[['Name1', 'Name2']].values.ravel())
df2.loc[~df2['Name'].isin(names), 'Y'] = np.nan
Name Y
0 Jack 0.0
1 John 1.0
2 Sarah 0.0
3 Tom 1.0
4 Laura NaN
answered Nov 15 '18 at 17:12
VaishaliVaishali
22.7k41438
answered Nov 15 '18 at 17:12
VaishaliVaishali
22.7k41438
answered Nov 15 '18 at 17:12
VaishaliVaishali
22.7k41438
answered Nov 15 '18 at 17:12
VaishaliVaishali
22.7k41438
22.7k41438
add a comment |
add a comment |
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Please share your dfs as
df.to_dict()– user32185
Nov 15 '18 at 11:51