I have a dataframe, say it has columns "A" through "Z", and several thousand rows. I have also two same-length lists, each with dataframe indices. The first list represents the indices of rows that are to be written to, and the second list represents in order the indices of rows to be read from. At the same time I only want to copy specific columns, say "N" through "Z", which are not all of the same dtype (i.e., some floating point numbers, some booleans, some timestamps).
Is there a way to do the copy without resorting to for loops?
I've read up on joins, merges, concats, merge_asof, use of the .loc attribute, etc. None of them seem to address quite what I'm looking for, nor do any of the searches I've done on stackoverflow.
Related
I need to take values from a larger database, coor, and add them to an existing column in a smaller one, xshooter, where the entries match. I'm using RA columns in both to find the matching instances. I need a way to rewrite the for loops using pandas functions to reduce runtime.
for j in coor.index:
for i in xshooter_master_tabl.index:
if coor['RA_ICRS'][j] == xshooter_master_tabl['ra'][i]:
xshooter_master_tabl['star_id'][i] = coor['SimbadName'][j]
I have a pandas dataframe that I've extracted from a json object using pd.json_normalize.
It has 4 rows and over 60 columns, and with the exception of the 'ts' column there are no columns where there is more than one value.
Is it possible to merge the four rows togather to give one row which can then be written to a .csv file? I have searched the documentation and found no information on this.
To give context, the data is a one time record from a weather station, I will have records at 5 minute intervals and need to put all the records into a database for further use.
I've managed to get the desired result, it's a little convoluted, and i would expect that there is a much more succint way to do it, but I basically manipulated the dataframe, replaced all nan's with zero, replaced some strings with ints and added the columns together as shown in the code below:
with open(fname,'r') as d:
ws=json.loads(next(d))
df=pd.json_normalize(ws['sensors'], record_path='data')
df3=pd.concat([df.iloc[0],df.iloc[1], df.iloc[2],
df.iloc[3]],axis=1)
df3.rename(columns={0 :'a', 1:'b', 2 :'c' ,3 :'d'}, inplace=True)
df3=df3.fillna(0)
df3.loc['ts',['b','c','d']]=0
df3.loc[['ip_v4_gateway','ip_v4_netmask','ip_v4_address'],'c']=int(0)
df3['comb']=df3['a']+df3['b']+df3['c']+df3['d']
df3.drop(columns=['a','b','c','d'], inplace=True)
df3=df3.T
As has been said by quite a few people, the documentation on this is very patchy, so I hope this may help someone else who is struggling with this problem! (and yes, i know that one line isn't indented properly, get over it!)
I have a large dataframe (14,000 rows). The columns include 'title', 'x' and 'y' as well as other random data.
For a particular title, I've written a code which basically performs an analysis using the x and y values for a subset of this data (but the specifics are unimportant for this).
For this title (which is something like "Part number Y1-17") there are about 80 rows.
At the moment I have only worked out how to get my code to work on 1 subset of titles (i.e. one set of rows with the same title) at a time. For this I've been making a smaller dataframe out of my big one using:
df = pd.read_excel(r"mydata.xlsx")
a = df.loc[df['title'].str.contains('Y1-17')]
But given there are about 180 of these smaller datasets I need to do this analysis on, I don't want to have to do it manually.
My question is, is there a way to make all of the smaller dataframes automatically, by slicing the data by the unique 'title' value? All the help I've found, it seems like you need to specify the 'title' to make a subset. I want to subset all of it and I don't want to have to list all the title names to do it.
I've searched quite a lot and haven't found anything, however I am a beginner so it's very possible I've missed some really basic way of doing this.
I'm not sure if its important information but the modules I'm working with pandas, and numpy
Thanks for any help!
You can use Pandas groupby
For example:
df_dict = {key: title for key, title in df.copy().groupby('title', sort=False)}
Which creates a dictionary of DataFrames each containing all the columns and only the rows pertaining to each unique value of title.
I'm using elasticsearch_dsl to make make queries for and searches of an elasticsearch DB.
One of the fields I'm querying is an address, which as a structure like so:
address.first_line
address.second_line
address.city
adress.code
The returned documents hold this in JSON structures, such that the address is held in a dict with a field for each sub-field of address.
I would like to put this into a (pandas) dataframe, such that there is one column per sub-field of the address.
Directly putting address into the dataframe gives me a column of address dicts, and iterating the rows to manually unpack (json.normalize()) each address dict takes a long time (4 days, ~200,000 rows).
From the docs I can't figure out how to get elasticsearch_dsl to return flattened results. Is there a faster way of doing this?
Searching for a way to solve this problem, I've come across my own answer and found it lacking, so will update with a better way
Specifically: pd.json_normalize(df['json_column'])
In context: pd.concat([df, pd.json_normalize(df['json_column'])], axis=1)
Then drop the original column if required.
Original answer from last year that does the same thing much more slowly
df.column_of_dicts.apply(pd.Series) returns a DataFrame with those dicts flattened.
pd.concat(df,new_df) gets the new columns onto the old dataframe.
Then delete the original column_of_dicts.
pd.concat([df, df.address.apply(pd.Series)], axis=1) is the actual code I used.
One struggle I have with using Python Pandas is to repeat the same coding scheme for a large number of columns. For example, below is trying to create a new column age_b in a data frame called data. How do I easily loop through a long (100s or even 1000s) of numeric columns, do the exact same thing, with the newly created column names being the existing name with a prefix or suffix string such as "_b".
labels = [1,2,3,4,5]
data['age_b'] = pd.cut(data['age'],bins=5, labels=labels)
In general, I have many simply data frame column manipulations or calculations, and it's easy to write the code. However, so often I want to repeat the same process for dozens of columns, that's when I get bogged down, because most functions or manipulations would work for one column, but not easily repeatable to many columns. It would be nice if someone can suggest a looping code "structure". Thanks!