I have two df's, one for user names and another for real names. I'd like to know how I can check if I have a real name in my first df using the data of the other, and then replace it.
For example:
import pandas as pd
df1 = pd.DataFrame({'userName':['peterKing', 'john', 'joe545', 'mary']})
df2 = pd.DataFrame({'realName':['alice','peter', 'john', 'francis', 'joe', 'carol']})
df1
userName
0 peterKing
1 john
2 joe545
3 mary
df2
realName
0 alice
1 peter
2 john
3 francis
4 joe
5 carol
My code should replace 'peterKing' and 'joe545' since these names appear in my df2. I tried using pd.contains, but I can only verify if a name appears or not.
The output should be like this:
userName
0 peter
1 john
2 joe
3 mary
Can someone help me with that? Thanks in advance!
You can use loc[row, colum], here you can see the documentation about loc method. And Series.str.contain method to select the usernames you need to replace with the real names. In my opinion, this solution is clear in terms of readability.
for real_name in df2['realName'].to_list():
df1.loc[ df1['userName'].str.contains(real_name), 'userName' ] = real_name
Output:
userName
0 peter
1 john
2 joe
3 mary
Related
I have the following toy dataset df:
import pandas as pd
data = {
'id' : [1, 2, 3],
'name' : ['John Smith', 'Sally Jones', 'William Lee']
}
df = pd.DataFrame(data)
df
id name
0 1 John Smith
1 2 Sally Jones
2 3 William Lee
My ultimate goal is to add a column that represents a Google search of the value in the name column.
I do this using:
def create_hyperlink(search_string):
return f'https://www.google.com/search?q={search_string}'
df['google_search'] = df['name'].apply(create_hyperlink)
df
id name google_search
0 1 John Smith https://www.google.com/search?q=John Smith
1 2 Sally Jones https://www.google.com/search?q=Sally Jones
2 3 William Lee https://www.google.com/search?q=William Lee
Unfortunately, newly created google_search column is returning a malformed URL. The URL should have a "+" between the first name and last name.
The google_search column should return the following:
https://www.google.com/search?q=John+Smith
It's possible to do this using split() and join().
foo = df['name'].str.split()
foo
0 [John, Smith]
1 [Sally, Jones]
2 [William, Lee]
Name: name, dtype: object
Now, joining them:
df['bar'] = ['+'.join(map(str, l)) for l in df['foo']]
df
id name google_search foo bar
0 1 John Smith https://www.google.com/search?q=John Smith [John, Smith] John+Smith
1 2 Sally Jones https://www.google.com/search?q=Sally Jones [Sally, Jones] Sally+Jones
2 3 William Lee https://www.google.com/search?q=William Lee [William, Lee] William+Lee
Lastly, creating the updated google_search column:
df['google_search'] = df['bar'].apply(create_hyperlink)
df
Is there a more elegant, streamlined, Pythonic way to do this?
Thanks!
Rather than reinvent the wheel and modify your string manually, use a library that's guaranteed to give you the right result :
from urllib.parse import quote_plus
def create_hyperlink(search_string):
return f"https://www.google.com/search?q={quote_plus(search_string)}"
Use Series.str.replace:
df['google_search'] = 'https://www.google.com/search?q=' + \
df.name.str.replace(' ','+')
print(df)
id name google_search
0 1 John Smith https://www.google.com/search?q=John+Smith
1 2 Sally Jones https://www.google.com/search?q=Sally+Jones
2 3 William Lee https://www.google.com/search?q=William+Lee
I have DF that looks like below:
columna_1 column_2
1 I am Thomas
2 Are you Thomas
3 How are you?
4 I am fine...
5 Jack, what's up?
and I have a list like this:
names = ["Thomas", "Jack"]
What I need is to find values from list names if there are in column_2 and if there are, it should add new column with the values from list. So it should be like this:
columna_1 column_2 column_3
1 I am Thomas Thomas
2 Are you Thomas Thomas
5 Jack, what's up? Jack
I have started with finding just values but I am not sure how I could add a new column with correct values. Below is my code:
df[df['column_2'].astype("str").apply(lambda x: set(names).issubset(x))]
Do you have any idea? Thanks for help!
Use a regex:
import re
regex = fr'\b({"|".join(map(re.escape, names))})\b'
df['column_3'] = df['column_2'].str.extract(regex, expand=False)
To also drop the non matches:
import re
regex = fr'\b({"|".join(map(re.escape,names))})\b'
(df.assign(column_3=df['column_2'].str.extract(regex, expand=False))
.dropna(subset=['column_3'])
)
output:
columna_1 column_2 column_3
0 1 I am Thomas Thomas
1 2 Are you Thomas Thomas
4 5 Jack, what's up? Jack
NB. Note that in case both Thomas and Jack are in the sentence, only the first one in the sentence will match
for example, there is a column in a dataframe, 'ID'.
One of the entries is for example, '13245993, 3004992'
I only want to get '13245993'.
That also applies for every row in column 'ID'.
How to change the data in each row in column 'ID'?
You can try like this, apply slicing on ID column to get the required result. I am using 3 chars as no:of chars here
import pandas as pd
data = {'Name':['Tom', 'nick', 'krish', 'jack'],
'ID':[90877, 10909, 12223, 12334]}
df=pd.DataFrame(data)
print('Before change')
print(df)
df["ID"]=df["ID"].apply(lambda x: (str(x)[:3]))
print('After change')
print(df)
output
Before change
Name ID
0 Tom 90877
1 nick 10909
2 krish 12223
3 jack 12334
After change
Name ID
0 Tom 908
1 nick 109
2 krish 122
3 jack 123
You could do something like
data[data['ID'] == '13245993']
this will give you the columns where ID is 13245993
More Indepth Code
I hope this answers your question if not please let me know.
With best regards
I have a pandas dataframe that includes a "Name" column. Strings in the Name column may contain "Joe", "Bob", or "Joe Bob". I want to add a column for the type of person: just Joe, just Bob, or Both.
I was able to do this by creating boolean columns, turning them into strings, combining the strings, and then replacing the values. It just...didn't feel very elegant! I am new to Python...is there a better way to do this?
My original dataframe:
df = pd.DataFrame(data= [['Joe Biden'],['Bobby Kennedy'],['Joe Bob Briggs']], columns = ['Name'])
0
Name
1
Joe Biden
2
Bobby Kennedy
3
Joe Bob Briggs
I added two boolean columns to find names:
df['Joe'] = df.Name.str.contains('Joe')
df['Joe'] = df.Joe.astype('int')
df['Bob'] = df.Name.str.contains('Bob')
df['Bob'] = df.Bob.astype('int')
Now my dataframe looks like this:
df = pd.DataFrame(data= [['Joe Biden',1,0],['Bobby Kennedy',0,1],['Joe Bob Briggs',1,1]], columns = ['Name','Joe', 'Bob'])
0
Name
Joe
Bob
1
Joe Biden
1
0
2
Bobby Kennedy
0
1
3
Joe Bob Briggs
1
1
But what I really want is one "Type" column with categorical values: Joe, Bob, or Both.
To do that, I added a column to combine the booleans, then I replaced the values:
df["Type"] = df["Joe"].astype(str) + df["Bob"].astype(str)
0
Name
Joe
Bob
Type
1
Joe Biden
1
0
10
2
Bobby Kennedy
0
1
1
3
Joe Bob Briggs
1
1
11
df['Type'] = df.Type.astype('str') df['Type'].replace({'11': 'Both', '10': 'Joe','1': 'Bob'}, inplace=True)
0
Name
Joe
Bob
Type
1
Joe Biden
1
0
Joe
2
Bobby Kennedy
0
1
Bob
3
Joe Bob Briggs
1
1
Both
This feels clunky. Anyone have a better way?
Thanks!
You can use np.select to create the column Type.
You need to ordered correctly your condlist from the most precise to the widest.
df['Type'] = np.select([df['Name'].str.contains('Joe') & df['Name'].str.contains('Bob'),
df['Name'].str.contains('Joe'),
df['Name'].str.contains('Bob')],
choicelist=['Both', 'Joe', 'Bob'])
Output:
>>> df
Name Type
0 Joe Biden Joe
1 Bobby Kennedy Bob
2 Joe Bob Briggs Both
I am trying to map owners to an IP address through the use of two tables, df1 & df2. df1 contains the IP list to be mapped and df2 contains an IP, an alias, and the owner. After running a join on the IP column, it gives me a half joined dataframe. Most of the remaining data can be joined by replacing the NaN values with a join on the Alias column, but I can’t figure out how to do it.
My initial thoughts were to try nesting pd.merge inside fillna(), but it won't accept a dataframe. Any help would be greatly appreciated.
df1 = pd.DataFrame({'IP' : ['192.18.0.100', '192.18.0.101', '192.18.0.102', '192.18.0.103', '192.18.0.104']})
df2 = pd.DataFrame({'IP' : ['192.18.0.100', '192.18.0.101', '192.18.1.206', '192.18.1.218', '192.18.1.118'],
'Alias' : ['192.18.1.214', '192.18.1.243', '192.18.0.102', '192.18.0.103', '192.18.1.180'],
'Owner' : ['Smith, Jim', 'Bates, Andrew', 'Kline, Jenny', 'Hale, Fred', 'Harris, Robert']})
new_df = pd.DataFrame(pd.merge(df1, df2[['IP', 'Owner']], on='IP', how= 'left'))
Expected output is:
IP Owner
192.18.0.100 Smith, Jim
192.18.0.101 Bates, Andrew
192.18.0.102 Kline, Jenny
192.18.0.103 Hale, Fred
192.18.0.104 nan
No need to merge, Just pull data where condition satisfies. This is way faster than merge and less complicated.
condition = (df1['IP'] == df2['IP']) | (df1['IP'] == df2['Alias'])
df1['Owner'] = np.where(condition, df2['Owner'], np.nan)
print(df1)
IP Owner
0 192.18.0.100 Smith, Jim
1 192.18.0.101 Bates, Andrew
2 192.18.0.102 Kline, Jenny
3 192.18.0.103 Hale, Fred
4 192.18.0.104 NaN
Try this one:
new_df = pd.DataFrame(pd.merge(df1, pd.concat([df2[['IP', 'Owner']], df2[['Alias', 'Owner']].rename(columns={"Alias": "IP"})]).drop_duplicates(), on='IP', how= 'left'))
The result:
>>> new_df
IP Owner
0 192.18.0.100 Smith, Jim
1 192.18.0.101 Bates, Andrew
2 192.18.0.102 Kline, Jenny
3 192.18.0.103 Hale, Fred
4 192.18.0.104 NaN
Let's melt then use map:
df1['IP'].map(df2.melt('Owner').set_index('value')['Owner'])
Output:
0 Smith, Jim
1 Bates, Andrew
2 Kline, Jenny
3 Hale, Fred
4 NaN
Name: IP, dtype: object