I have a dataframe with 100s of columns and 1000s of rows but the basic structure is
Index 0 1 2
0 AAA NaN AAA
1 NaN BBB NaN
2 NaN NaN CCC
3 DDD DDD DDD
I would like to add two new columns one would be and id which would be equal to the first value in each row the second would be a count of the values in each row. It would look like this. To be clear all rows will always have the same value.
Index id count 0 1 2
0 AAA 2 AAA NaN AAA
1 BBB 1 NaN BBB NaN
2 CCC 1 NaN NaN CCC
3 DDD 3 DDD DDD DDD
Any help in figuring out a way to do this would be greatly appreciated. Thanks
This should work.
df['id'] = df.bfill(axis=1).iloc[:, 0].fillna('All NANs')
df['count'] = df.drop(columns=["id"]).notnull().sum(axis=1)
To maintain the order of columns:
df = df[list(df.columns[-2:]) + list(df.columns[:-2])]
Create the Dataframe
test_df = pd.DataFrame([['AAA',np.nan,'AAA'], [np.nan,'BBB',np.nan], [np.nan,np.nan, 'CCC'], ['DDD','DDD','DDD']])
Count the non-NaN elements in each row as count
test_df['count'] = test_df.notna().sum(axis=1)
Option-1: Select the first element in the row as id (regardless of NaN value)
test_df['id'] = test_df[0]
Option-2: Select the first non-NaN element as id for each row
test_df['id'] = test_df.apply(lambda x: x[x.first_valid_index()], axis=1)
Related
I have two DataFrames with one common column as the key and I want to perform a VLOOKUP sort of operation to fetch values from the first DataFrame corresponding to the keys in second DataFrame.
DataFrame 1
key value
0 aaa 111
1 bbb 222
2 ccc 333
DataFrame 2
key value
0 bbb None
1 ccc 333
2 aaa None
3 aaa 111
Desired Output
key value
0 bbb 222
1 ccc 333
2 aaa 111
3 aaa 111
I do not want to use merge as both of my DFs might have NULL values for the key column and since pandas merge behave differently than sql join, all such rows might get joined with each other.
I tried below approach
DF2['value'] = np.where(DF2['key'].isnull(), DF1.loc[DF2['key'].equals(DF1['key'])]['value'], DF2['value'])
but have been getting KeyError: False error.
You can use:
df2['value'] = df2['value'].fillna(df2['key'].map(df1.set_index('key')['value']))
print(df2)
# Output
key value
0 bbb 222
1 ccc 333
2 aaa 111
3 aaa 111
I have a pandas dataframe that looks something like this:
id
col1
col2
value1
value2
value3
1
123456
1234ABC
1
2
nan
1
123456
1234567
1
2
nan
1
124567
1234568
1
2
nan
1
124567
2345678
nan
2
nan
2
123456
1234564
nan
2
nan
2
123456
2132534
nan
2
nan
2
543210
10580701
nan
2
nan
I want to make a function that runs through the whole set and cleans it with these conditions:
For every unique id, do the following steps:
If col 1 has 6 digit code and col 2 has number and letter combination:
Then keep row
If col 1 has 6 digit code and col 2 has something else than number and letter combination
Then keep the first row with same 6 digit code in col1.
So in this table example, after running the function, these rows would still be in the dataset:
id
col1
col2
value1
value2
value3
1
123456
1234ABC
1
2
nan
1
123456
1234567
1
2
nan
1
124567
2345678
nan
2
nan
2
123456
1234564
nan
2
nan
2
543210
10580701
nan
2
nan
At first i tried something like this:
def process_df(df):
# Sort the dataframe by column 1 and column 2
df = df.sort_values(by=['col1', 'col2'])
# Create a new column that indicates whether a row has a letter in column 2
df['has_letter'] = df['col2'].str.contains('[a-zA-Z]')
# Group the dataframe by column 1 and apply the following function to each group
def group_func(group):
# If there are any rows with a letter in column 2, keep all of them
if group['has_letter'].any():
return group
# If there are no rows with a letter in column 2, keep the first row
else:
return group.iloc[0:1]
df = df.groupby('col1').apply(group_func)
# Drop the has_letter column
df = df.drop(columns=['has_letter'])
df=df.reset_index(drop=True)
return df
But it didn't work since every unique id might have rows where col1 6 digit code is same than some other ids col1 6 digit code
So somehow I have to make a function that does this to every id separately so it would work.
EDIT:
I edited the
df = df.groupby('col1').apply(group_func)
row to
df = df.groupby(['id', 'col1']).apply(group_func)
This seems? to do the job.
First I grouped by id, then I extracted the rows that had letters in col2, Iterating on the groups I further grouped by col1 and from these I extracted the first occurrence of the col1 value.
I hope this can help you.
# Sort the dataframe by column 1 and column 2
df4 = df4.sort_values(by=['col1', 'col2'])
# Create a new column that indicates whether a row has a letter in column 2
df4['has_letter'] = df4['col2'].str.contains('[a-zA-Z]')
# Fillna
df4['has_letter'] = df4['has_letter'].fillna(False)
grouped_id = df4.groupby('id')
output_df = pd.DataFrame()
# Iterate over group_id
for name, group_id in grouped_id:
output_df = output_df.append(group_id[group_id['has_letter']])
no_col2_letter = group_id[group_id['has_letter']==False]
grouped_col2 = no_col2_letter.groupby('col1')
for name, group_col2 in grouped_col2:
output_df = output_df.append(group_col2[:1])
output_df
How to add two text rows to one and keep other rows same as before pandas
How to do that by pandas?
original dataframe:
textA TextB
0 a zz
1 bbb zzzzz
2 ccc zzz
desired output is:
textA TextB
0 a bbb zz
1 bbb zzzzz
2 ccc zzz
i mean i just add two row text to specific row and other rows keep
original values
Do you mean by something like:
>>> df.loc[0, 'textA'] += ' ' + df.loc[1, 'textA']
>>> df
textA TextB
0 a bbb zz
1 bbb zzzzz
2 ccc zzz
>>>
I have 2 dataframes which look like this:
df1
A B
AAA 50
BBB 100
CCC 200
df2
C D
CCC 500
AAA 10
EEE 2100
I am trying to output the dataset where column E would be B - D if A = C. Since A values are not aligned with C values I cant seem to find the appropriate method to apply calculations and compare the right numbers.
There also are values which are not shared between two datasets in this case I want to add text value 'not found' in those places so that the output would look like this:
output
A B C D E
AAA 50 AAA 10 B-D
BBB 100 Not found Not found Not found
CCC 200 CCC 500 B-D
Not found Not found EEE 2100 Not found
Thank you for your suggestions.
Use outer join with left_on and right_on parameters with DataFrame.merge and then subtract columns, for possible subtract numeric is better use missing values:
df = (df1.merge(df2, left_on='A', right_on='C', how='outer')
.fillna({'A':'Not found', 'C':'Not found'})
.assign(E = lambda x: x.B - x.D))
print (df)
A B C D E
0 AAA 50.0 AAA 10.0 40.0
1 BBB 100.0 Not found NaN NaN
2 CCC 200.0 CCC 500.0 -300.0
3 Not found NaN EEE 2100.0 NaN
Last is possible replace all missing values, only numeric columns are now mixed - strings with numbers, so next processing like some arithmetic operations is problematic:
df = (df1.merge(df2, left_on='A', right_on='C', how='outer')
.assign(E = lambda x: x.B - x.D)
.fillna('Not found'))
print (df)
A B C D E
0 AAA 50 AAA 10 40
1 BBB 100 Not found Not found Not found
2 CCC 200 CCC 500 -300
3 Not found Not found EEE 2100 Not found
I am new to pandas and I am trying to get a list of values that exists in both columns, values that exist in column A, values that only exist in column B.
My .csv file looks like this:
A B
AAA ZZZ
BBB BBB
CCC EEE
DDD FFF
EEE AAA
DDD
GGG HHH
JJJ
Columns have a different length and my outcome would be 3 lists or one csv that I would ouput having 3 columns, one for items existing in both columns, one for items existing in only A column and one for items existing in only B column.
IN BOTH IN COLUMN A IN COLUMN B
AAA CCC ZZZ
BBB GGG FFF
DDD JJJ HHH
EEE
(empty one)
I have tried using .isin() module but it returns true of false rather than the actual list.
existing_in_both = df_column_a.isin(df_column_b)
And I do not know how I should try to extract values that only exist in either column A or B.
Thank you for your suggestions.
My actual .csv has the following:
id clickout_id timestamp click_id click_type
1 123abc 2019-11-25 c51c56d1 1
1 123dce 2019-11-25 c51c5fs1 12
and other file is looking like this:
timestamp id gid type
2019-11-25 1 c51c56d1 2
2019-11-25 1 c51c5fs1 2
And I am trying to compare click_id from first file and gid from the second file.
When I print out using your answer I get the header names as answers rather than the values from the columns.
Use sets with intersection and difference, then for new DataFrame are used Series, because different lengths of outputs:
a = set(df.A)
b = set(df.B)
df = pd.DataFrame({'IN BOTH': pd.Series(list(a & b)),
'IN COLUMN A': pd.Series(list(a - b)),
'IN COLUMN B': pd.Series(list(b - a))})
print (df)
IN BOTH IN COLUMN A IN COLUMN B
0 DDD CCC FFF
1 BBB GGG ZZZ
2 AAA JJJ HHH
3 NaN NaN
4 EEE NaN NaN
Or use numpy.intersect1d with numpy.setdiff1d:
df = pd.DataFrame({'IN BOTH': pd.Series(np.intersect1d(df.A, df.B)),
'IN COLUMN A': pd.Series(np.setdiff1d(df.A, df.B)),
'IN COLUMN B': pd.Series(np.setdiff1d(df.B, df.A))})
print (df)
IN BOTH IN COLUMN A IN COLUMN B
0 CCC FFF
1 AAA GGG HHH
2 BBB JJJ ZZZ
3 DDD NaN NaN
4 EEE NaN NaN