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How do I separate this dataframe column by month?

A few rows of my dataframe
The third column shows the time of completion of my data. Ideally, I'd want the second row to just show the date, removing the second half of the elements, but I'm not sure how to change the elements. I was able to change the (second) column of strings into a column of floats without the pound symbol in order to find the sum of costs. However, this column has no specific keyword I just select for all of the elements to remove.
Second part of my question is is it is possible to easy create another dataframe that contains 2021-05-xx or 2021-06-xx. I know there's a way to make another dataframe selecting certain rows like the top 15 or bottom 7. But I don't know if there's a way to make a dataframe finding what I mentioned. I'm thinking it follows the Series.str.contains(), but it seems like when I put '2021-05' in the (), it shows a entire dataframe of False's.

Extracting just the date and ignoring the time from the datetime column can be done by changing the formatting of the column.
df['date'] = pd.to_datetime(df['date']).dt.date
To the second part of the question about creating a new dataframe that is filtered down to only contain rows between 2021-05-xx and 2021-06-xx, we can use pandas filtering.
df_filtered = df[(df['date'] >= pd.to_datetime('2021-05-01')) & (df['date'] <= pd.to_datetime('2021-06-30'))]
Here we take advantage of two things: 1) Pandas making it easy to compare the chronology of different dates using numeric operators. 2) Us knowing that any date that contains 2021-05-xx or 2021-06-xx must come on/after the first day of May and on/before the last day of June.
There are also a few GUI's that make it easy to change the formatting of columns and to filter data without actually having to write the code yourself. I'm the creator of one of these tools, Mito. To filter dates in Mito, you can just enter the dates using our calendar input fields and Mito will generate the equivalent pandas code for you!

Inserting column(s) into a python pandas dataframe in between other columns using the other column's names as the location?

I have an excel file that I've converted into a dataframe. I would like to insert a column (or two depending on the situation) in between other columns.
For example:
I have columns:
Table-Chair-Bed
I want to insert column grass and column water in between Chair and Bed. I have tried:
df.insert(loc=2, column='grass', value='')
df.insert(loc=3, column='water', value='')
This does work but what if the columns change from the data source some of the time and the columns are like this: Couch-Kitchen-Table-Chair-Bed
I still want to insert these new columns in between Chair and Bed but don't want to have to re-write the code every time (because...automation). Is there a way to have the code look for the column names and insert the new columns in between them without using the location number, that way the column order or number of columns won't matter.

You can find the position of the 'Chair' column and then add them after that.
df.insert(df.columns.get_loc('Chair') + 1, column='grass', value='')

cLoc1 = df.columns.get_loc("Chair")
cLoc2 = df.columns.get_loc("Bed")
df.insert(loc=cLoc1, column='grass', value='')
df.insert(loc=cLoc2, column='water', value='')
Basically, you get the location of the column you are looking for and then pass it on to your code.

How to keep rows in a DataFrame based on column unique sets?

How to keep rows in a DataFrame based on column unique pairs in Python?
I have a massive ocean datasets with over 300k rows. Given some unique latitude-longitude pairs have multiple depths, I am only interested in keeping unique rows that contain unique sets of Latitude-Longitude-Year-Month.
The goal here is to know how many months of sampling for a given Latitude-Longitude location.
I tried using pandas conditions but the sets that I want are dependent on each other.
Any ideas on how to do this?
So far I've tried the following:
# keep Latitude, Longitude, Year and Month
glp = glp[['latitude', 'longitude', 'year', 'month']]
# only keep unique rows
glp.drop_duplicates(keep = False, inplace = True)
but it removes too many lines as I want those four variables to work together

The code you are looking for is .drop_duplicates()
Assuming your dataframe variable is df, you can use
df.drop_duplicates()
or include column name list if you're only looking for unique values within specified columns
df.drop_duplicates(subset=[column_list])#column_list of names you want to compare
Edit:
If that's the case, I guess you could just do
df.groupby([column_list]).first() #first() takes the first values of other columns
And then you could just use df.reset_index() if you want the unique sets as columns again.

Create Loop to dynamically select rows from dataframe, then append selected rows to another dataframe: df.query()

I am currently working with dataframes in pandas. In sum, I have a dataframe called "Claims" filled with customer claims data, and I want to parse all the rows in the dataframe based on the unique values found in the field 'Part ID.' I would then like to take each set of rows and append it one at a time to an empty dataframe called "emptydf." This dataframe has the same column headings as the "Claims" dataframe. Since the values in the 'Part ID' column change from week to week, I would like to find some way to do this dynamically, rather than comb through the dataframe each week manually. I was thinking of somehow incorporating the df.where() expression and a For Loop, but am at a loss as to how to put it all together. Any insight into how to go about this, or even some better methods, would be great! The code I have thus far is divided into two steps as follows:
emptydf = Claims[0:0]
#Create empty dataframe
2.Parse_Claims = Claims.query('Part_ID == 1009')
emptydf = emptydf.append(Parse_Claims)
#Parse the dataframe by each unique Part ID number and append to empty dataframe. As you can see, I can only hard code one Part ID number at a time so far. This would take hours to complete manually, so I would love to figure out a way to iterate through the Part ID column and append the data dynamically.
Needless to say, I am super new to Python, so I definitely appreciate your patience in advance!

empty_df = list(Claims.groupby(Claims['Part_ID']))
this will create a list of tuples one for each part id. each tuple has 2 elements 1st is part id and 2nd is subset for that part id

combine the values of a specific column of a dataframe in one row or unit

I want to combine the different values/rows of a certain column. these values are texts and I want to combine them together to perform word count and find the most common words.
the dataframe is called df and is made of 30 columns. I want to combine all the rows of the first column (labeled 'text') into one row, or one list etc,. it doesn't matter as long as I can perform FreqDist on it. I am not interested in grouping the values according to a certain value, I just want all the values in this column to become one block.
I looked around a lot and I couldn't find what I am looking for.
thanks a lot.