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Pandas Merging 101
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We have two dataframes exported from Excel. Both have a column called "PN", which was set at the exporting. "First" and "Second" are the variables with those dataframes. "Third" stores a list of coinsidences between the 2 "PN" columns. Pandas Merge method worked without such list, but since the thing now is not working, I added it as well.
gnida = []
for h in first['PN']:
for u in zip(second['PN'], second['P']):
if h==u[0]:
gnida.append(u)
third = pd.DataFrame(gnida)
I need values in the second dataframe to be placed on the rows where coinsidence occurs. If I simply merge:
fourth = first.merge(second)
, columns that have names other than in the first df are added, but the output is 1 row of headings without rows with values.
If I merge
fourth = first.merge(third)
, I get:
No common columns to perform merge on. Merge options: left_on=None, right_on=None, left_index=False, right_index=False.
If I state further "left on = "PN", I get:
object of type 'NoneType' has no len().
Thus, how can Merge or Join or whatever the 2 dataframes in order to use one column of the second dataframe as a key, placing values in a new column where coinsidence occurs. Thank you
if you wish to merge by the index, just use fourth = first.join(third)
otherwise, you need to create a dataframe from third, add the column that you want to merge by, and use:
fourth = first.merge(third,on='name_of_the_column')
Related
I have two Dataframes, one containing my data read in from a CSV file and another that has the data grouped by all of the columns but the last and reindexed to contain a column for the count of the size of the groups.
df_k1 = pd.read_csv(filename, sep=';')
columns_for_groups = list(df_k1.columns)[:-1]
k1_grouped = df_k1.groupby(columns_for_groups).size().reset_index(name="Count")
I need to create a series such that every row(i) in the series corresponds to row(i) in my original Dataframe but the contents of the series need to be the size of the group that the row belongs to in the grouped Dataframe. I currently have this, and it works for my purposes, but I was wondering if anyone knew of a faster or more elegant solution.
size_by_row = []
for row in df_k1.itertuples():
for group in k1_grouped.itertuples():
if row[1:-1] == group[1:-1]:
size_by_row.append(group[-1])
break
group_size = pd.Series(size_by_row)
I have a dataframe with multiple columns as t_orno,t_pono, t_sqnb ,t_pric,....and so on(it's a table with multiple columns).
The 2nd dataframe contains certain name of the columns from 1st dataframe. Eg.
columnname
t_pono
t_pric
:
:
I need to select only those columns from the 1st dataframe whose name is present in the 2nd. In above example t_pono,t_pric.
How can this be done?
Let's say you have the following columns (which can be obtained using df.columns, which returns a list):
df1_cols = ["t_orno", "t_pono", "t_sqnb", "t_pric"]
df2_cols = ["columnname", "t_pono", "t_pric"]
To get only those columns from the first dataframe that are present in the second one, you can do set intersection (and I cast it to a list, so it can be used to select data):
list(set(df1_cols).intersection(df2_cols))
And we get the result:
["t_pono", "t_pric"]
To put it all together and select only those columns:
select_columns = list(set(df1_cols).intersection(df2_cols))
new_df = df1.select(*select_columns)
I have a very large data frame that I want to split ALL of the columns except first two based on a comma delimiter. So I need to logically reference column names in a loop or some other way to split all the columns in one swoop.
In my testing of the split method:
I have been able to explicitly refer to ( i.e. HARD CODE) a single column name (rs145629793) as one of the required parameters and the result was 2 new columns as I wanted.
See python code below
HARDCODED COLUMN NAME --
df[['rs1','rs2']] = df.rs145629793.str.split(",", expand = True)
The problem:
It is not feasible to refer to the actual column names and repeat code.
I then replaced the actual column name rs145629793 with columns[2] in the split method parameter list.
It results in an ERROR
'str has ni str attribute'
You can index columns by position rather than name using iloc. For example, to get the third column:
df.iloc[:, 2]
Thus you can easily loop over the columns you need.
I know what you are asking, but it's still helpful to provide some input data and expected output data. I have included random input data in my code below, so you can just copy and paste this to run, and try to apply it to your dataframe:
import pandas as pd
your_dataframe=pd.DataFrame({'a':['1,2,3', '9,8,7'],
'b':['4,5,6', '6,5,4'],
'c':['7,8,9', '3,2,1']})
import copy
def split_cols(df):
dict_of_df = {}
cols=df.columns.to_list()
for col in cols:
key_name = 'df'+str(col)
dict_of_df[key_name] = copy.deepcopy(df)
var=df[col].str.split(',', expand=True).add_prefix(col)
df=pd.merge(df, var, how='left', left_index=True, right_index=True).drop(col, axis=1)
return df
split_cols(your_dataframe)
Essentially, in this solution you create a list of the columns that you want to loop through. Then you loop through that list and create new dataframes for each column where you run the split() function. Then you merge everything back together on the index. I also:
included a prefix of the column name, so the column names did not have duplicate names and could be more easily identifiable
dropped the old column that we did the split on.
Just import copy and use the split_cols() function that I have created and pass the name of your dataframe.
I have a pandas dateframe of two columns ['company'] which is a string and ['publication_datetime'] which is a datetime.
I want to group by company and the publication_date , adding a new column with the maximum publication_datetime for each record.
so far i have tried:
issuers = news[['company','publication_datetime']]
issuers['publication_date'] = issuers['publication_datetime'].dt.date
issuers['publication_datetime_max'] = issuers.groupby(['company','publication_date'], as_index=False)['publication_datetime'].max()
my group by does not appear to work.
i get the following error
ValueError: Wrong number of items passed 3, placement implies 1
You need the transform() method to cast the result in the original dimension of the dataframe.
issuers['max'] = issuers.groupby(['company', 'publication_date'])['publication_datetime'].transform('max')
The result of your groupby() before was returning a multi-indexed group object, which is why it's complaining about 3 values (first group, second group, and then values). But even if you just returned the values, it's combining like groups together, so you'll have fewer values than needed.
The transform() method returns the group results for each row of the dataframe in a way that makes it easy to create a new column. The returned values are an indexed Series with the indices being the original ones from the issuers dataframe.
Hope this helps! Documentation for transform here
The thing is by doing what you are doing you are trying to set a DataFrame to a column value.
Doing the following will get extract only the values without the two indexe columns:
issuers['publication_datetime_max'] = issuers.groupby(['company','publication_date'], as_index=False)['publication_datetime'].max().tolist()
Hope this help !
I have a pandas data frame and I would like to duplicate those rows which meet some column condition (i.e. having multiple elements in CourseID column)
I tried iterating over the data frame to identify the rows which should be duplicated but i don't know how to duplicate them,
Using Pandas version 0.25 it is quite easy:
The first step is to split df.CourseID (converting each element to a list)
and then to explode it (break each list into multiple rows,
repeating other columns in each row):
course = df.CourseID.str.split(',').explode()
The result is:
0 456
1 456
1 799
2 789
Name: CourseID, dtype: object
Then, all to do is to join df with course, but in order to avoid
repeating column names, you have to drop original CourseID column before.
Fortunately, in can be expressed in a single instruction:
df.drop(columns=['CourseID']).join(course)
If you have some older version of Pandas this is a good reason to
upgrade it.