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Count occurrence of column values in other dataframe column

I have two dataframes and I want to count the occurrence of "classifier" in "fullname". My problem is that my script counts a word like "carrepair" only for one classifier and I would like to have a count for both classifiers. I would also like to add one random coordinate that matches the classifier.
First dataframe:
Second dataframe:
Result so far:
Desired Result:
My script so far:
import pandas as pd
fl = pd.read_excel (r'fullname.xlsx')
clas= pd.read_excel (r'classifier.xlsx')
fl.fullname= fl.fullname.str.lower()
clas.classifier = clas.classifier.str.lower()
pat = '({})'.format('|'.join(clas['classifier'].unique()))
fl['fullname'] = fl['fullname'].str.extract(pat, expand = False)
clas['count_of_classifier'] = clas['classifier'].map(fl['fullname'].value_counts())
print(clas)
Thanks!

You could try this:
import pandas as pd
fl = pd.read_excel (r'fullname.xlsx')
clas= pd.read_excel (r'classifier.xlsx')
fl.fullname= fl.fullname.str.lower()
clas.classifier = clas.classifier.str.lower()
# Add a new column to 'fl' containing either 'repair' or 'car'
for value in clas["classifier"].values:
fl.loc[fl["fullname"].str.contains(value, case=False), value] = value
# Count values and create a new dataframe
new_clas = pd.DataFrame(
{
"classifier": [col for col in clas["classifier"].values],
"count": [fl[col].count() for col in clas["classifier"].values],
}
)
# Merge 'fl' and 'new_clas'
new_clas = pd.merge(
left=new_clas, right=fl, how="left", left_on="classifier", right_on="fullname"
).reset_index(drop=True)
# Keep only expected columns
new_clas = new_clas.reindex(columns=["classifier", "count", "coordinate"])
print(new_clas)
# Outputs
classifier count coordinate
repair 3 52.520008, 13.404954
car 3 54.520008, 15.404954

overwriting the values of a column in python

I was trying to modify each string present in column named Date_time in a data-frame. The values(string type) present in that column is as:
"40 11-02-20 11:42:36"
I was trying to delete the characters until first space and trying to replace it with: "11-02-20 11:42:36". I was able to split the value but unable to rewrite it in the same cell of that column. Here is the code i have done so far:
import numpy as np
import matplotlib as plt
import pandas as pd
dataset = pd.read_csv('20-02-11.csv')
for i in dataset.itertuples():
print(type(i.Date_time))
str = i.Date_time
str1 = str.split(None,1)[1]
i.Date_time = str1
print(str1)
print(i.Date_time)
break
and it shows AttributeError when i am trying to assign str1 to i.Date_time.
Please help.

The tuples that itertuples() returns, can/should not be used to set values in the original dataframe. They are copies not the actual data of the dataframe. You can try something like this:
for i in range(len(dataset)):
your_string = dataset.loc[i, "Date_time"]
adjusted_string = your_string.split(None, 1)[1]
dataset.loc[i, "Date_time"] = adjusted_string
This will use the actual data stored in the dataframe.
Using the df.at()-function:
for i, row in dataset.iterrows():
your_string = row.Date_time # or row['Date_time']
adjusted_string = your_string.split(None, 1)[1]
dataset.at[i,'Date_time'] = adjusted_string

You can format the entire column at once. Starting with a dataframe like this:
df = pd.DataFrame({'date_time': ['40 11-02-20 11:42:36', '31 11-02-20 11:42:36']})
print(df)
returns
date_time
0 40 11-02-20 11:42:36
1 31 11-02-20 11:42:36
You can remove the first characters and space like this:
df['date_time'] = [i[1+len(i.split(' ')[0]):] for i in df['date_time']]
print(df)
returns
date_time
0 11-02-20 11:42:36
1 11-02-20 11:42:36

Column in DataFrame isn't recognised. Keyword Error: 'Date'

I'm in the initial stages of doing some 'machine learning'.
I'm trying to create a new data frame and one of the columns doesn't appear to be recognised..?
I've loaded an Excel file with 2 columns (removed the index). All fine.
Code:
df = pd.read_excel('scores.xlsx',index=False)
df=df.rename(columns=dict(zip(df.columns,['Date','Amount'])))
df.index=df['Date']
df=df[['Amount']]
#creating dataframe
data = df.sort_index(ascending=True, axis=0)
new_data = pd.DataFrame(index=range(0,len(df)),columns=['Date','Amount'])
for i in range(0,len(data)):
new_data['Date'][i] = data['Date'][i]
new_data['Amount'][i] = data['Amount'][i]
The error:
KeyError: 'Date'
Not really sure what's the problem here.
Any help greatly appreciated

I think in line 4 you reduce your dataframe to just one column "Amount"

To add to #Grzegorz Skibinski's answer, the problem is after line 4, there is no longer a 'Date' column. The Date column was assigned to the index and removed, and while the index has a name "Date", you can't use 'Date' as a key to get the index - you have to use data.index[i] instead of data['Date'][i].

It seems that you have an error in the formatting of your Date column.
To check that you don't have an error on the name of the columns you can print the columns names:
import pandas as pd
# create data
data_dict = {}
data_dict['Fruit '] = ['Apple', 'Orange']
data_dict['Price'] = [1.5, 3.24]
# create dataframe from dict
df = pd.DataFrame.from_dict(data_dict)
# Print columns names
print(df.columns.values)
# Print "Fruit " column
print(df['Fruit '])
This code outputs:
['Fruit ' 'Price']
0 Apple
1 Orange
Name: Fruit , dtype: object
We clearly see that the "Fruit " column as a trailing space. This is an easy mistake to do, especially when using excel.
If you try to call "Fruit" instead of "Fruit " you obtain the error you have:
KeyError: 'Fruit'

How to dynamically match rows from two pandas dataframes

I have a large dataframe of urls and a smaller 2nd dataframe that contains columns of strings which I want to use to merge the two dataframes together. Data from the 2nd df will be used to populate the larger 1st df.
The matching strings can contain * wildcards (and more then one) but the order of the grouping still matters; so "path/*path2" would match with "exsample.com/eg_path/extrapath2.html but not exsample.com/eg_path2/path/test.html. How can I use the strings in the 2nd dataframe to merge the two dataframes together. There can be more then one matching string in the 2nd dataframe.
import pandas as pd
urls = {'url':['https://stackoverflow.com/questions/56318782/','https://www.google.com/','https://en.wikipedia.org/wiki/Python_(programming_language)','https://stackoverflow.com/questions/'],
'hits':[1000,500,300,7]}
metadata = {'group':['group1','group2'],
'matching_string_1':['google','wikipedia*Python_'],
'matching_string_2':['stackoverflow*questions*56318782','']}
result = {'url':['https://stackoverflow.com/questions/56318782/','https://www.google.com/','https://en.wikipedia.org/wiki/Python_(programming_language)','https://stackoverflow.com/questions/'],
'hits':[1000,500,300,7],
'group':['group2','group1','group1','']}
df1 = pd.DataFrame(urls)
df2 = pd.DataFrame(metadata)
what_I_am_after = pd.DataFrame(result)

Not very robust but gives the correct answer for my example.
import pandas as pd
urls = {'url':['https://stackoverflow.com/questions/56318782/','https://www.google.com/','https://en.wikipedia.org/wiki/Python_(programming_language)','https://stackoverflow.com/questions/'],
'hits':[1000,500,300,7]}
metadata = {'group':['group1','group2'],
'matching_string_1':['google','wikipedia*Python_'],
'matching_string_2':['stackoverflow*questions*56318782','']}
result = {'url':['https://stackoverflow.com/questions/56318782/','https://www.google.com/','https://en.wikipedia.org/wiki/Python_(programming_language)','https://stackoverflow.com/questions/'],
'hits':[1000,500,300,7],
'group':['group2','group1','group1','']}
df1 = pd.DataFrame(urls)
df2 = pd.DataFrame(metadata)
results = pd.DataFrame(columns=['url','hits','group'])
for index,row in df2.iterrows():
for x in row[1:]:
group = x.split('*')
rx = "".join([str(x)+".*" if len(x) > 0 else '' for x in group])
if rx == "":
continue
filter = df1['url'].str.contains(rx,na=False, regex=True)
if filter.any():
temp = df1[filter]
temp['group'] = row[0]
results = results.append(temp)
d3 = df1.merge(results,how='outer',on=['url','hits'])

Save data frame from inside for loop

I have a function that takes in a dataframe and returns a (reduced) dataframe, e.g. like this:
def transforming_data(dataframe, col_1, col_2, normalized = True):
''' takes in dataframe, groups col_1 according to col_2 and returns dataframe
'''
df = dataframe[col_1].groupby(dataframe[col_2]).value_counts(normalize = normalized).unstack(fill_value = 0)
return dataframe
For the following code, this gives me:
import pandas as pd
import numpy as np
np.random.seed(12)
def transforming_data(df, col_1, col_2, normalized = True):
''' takes in df, groups col_1 according to col_2 and returns df '''
df = dataframe[col_1].groupby(dataframe[col_2]).value_counts(normalize = normalized).unstack(fill_value = 0)
return df
numrows = 1000
dataframe = pd.DataFrame({'Numerical': np.random.randn(numrows),
'Category': np.random.choice(['Panda', 'Elephant', 'Anaconda'], numrows),
'Response 1': np.random.choice(['Yes', 'Maybe', 'No', 'Don\'t know'], numrows),
'Response 2': np.random.choice(['Very Much', 'Much', 'A bit', 'Not at all'], numrows)})
test = transforming_data(dataframe, 'Response 1', 'Category')
print(test)
# Output
# Response 1 Don't know Maybe No Yes
# Category
# Anaconda 0.275229 0.232416 0.217125 0.275229
# Elephant 0.220588 0.270588 0.255882 0.252941
# Panda 0.258258 0.222222 0.273273 0.246246
So far, so good.
Now I want to use the function transforming_data inside a for loop for every column in dataframe (as I have lots of columns, not just two) and save the resulting dataframe to a new dataframe, e.g. test_response_1 and test_response_2 for this example.
Can someone point me in the right direction - i.e. how to implement the loop correctly?
So far, I am using something like this - but cannot figure out how to save the data frame
for column in dataframe.columns.tolist():
temp_df = transforming_data(dataframe, column, 'Category')
# here, I need to save tmp_df outside of the loop but don't know how to
Thanks a lot for pointers and help. (Note: the most similar question I found does not talk about actually saving the data frame, so it doesn't help me with this.

If you want to save (in memory) all of the temp_df's from your loop, you can append them to a list that you can then index afterwards:
temp_dfs = []
for column in dataframe.columns.tolist(): #you don't actually need the tolist() method here
temp_df = transforming_data(dataframe, column, 'Category')
temp_dfs.append(temp_df)
If you rather be able to access these temp_df's by the column name that was used to transform them, then you could assign each to a dictionary, using the column as the key:
temp_dfs = {}
for column in dataframe.columns.tolist():
temp_df = transforming_data(dataframe, column, 'Category')
temp_dfs[column] = temp_df
If by "save" you meant "write to disk", then you can use one of the many to_<file_format>() methods that pandas provides:
temp_dfs = {}
for column in dataframe.columns.tolist():
temp_df = transforming_data(dataframe, column, 'Category')
temp_df.to_csv('temp_df{}.csv'.format(column))
Here's the to_csv() docs.

The most simple solution would be to save the result dataframes into a list. Assuming that all columns that you want to loop over have the text Response in their column name:
result_dframes = []
for col_name in dataframe.filter(like='Response').columns:
result_dframe = transforming_data(dataframe, col_name, 'Category')
result_dframes.append(result_dframe)
Alternatively you can also obtain the exact same result with a list comprehension instead of a for-loop:
result_dframes = [
transforming_data(dataframe, col_name, 'Category')
for col_name in dataframe.filter(like='Response')
]