Say I have two DataFrames
df1 = pd.DataFrame({'A':[1,2], 'B':[3,4]}, index = [0,1])
df2 = pd.DataFrame({'B':[8,9], 'C':[10,11]}, index = [1,2])
I want to merge so that any values in df1 are overwritten in there is a value in df2 at that location and any new values in df2 are added including the new rows and columns.
The result should be:
A B C
0 1 3 nan
1 2 8 10
2 nan 9 11
I've tried combine_first but that causes only nan values to be overwritten
updated has the issue where new rows are created rather than overwritten
merge has many issues.
I've tried writing my own function
def take_right(df1, df2, j, i):
print (df1)
print (df2)
try:
s1 = df1[j][i]
except:
s1 = np.NaN
try:
s2 = df2[j][i]
except:
s2 = np.NaN
if math.isnan(s2):
#print(s1)
return s1
else:
# print(s2)
return s2
def combine_df(df1, df2):
rows = (set(df1.index.values.tolist()) | set(df2.index.values.tolist()))
#print(rows)
columns = (set(df1.columns.values.tolist()) | set(df2.columns.values.tolist()))
#print(columns)
df = pd.DataFrame()
#df.columns = columns
for i in rows:
#df[:][i]=[]
for j in columns:
df = df.insert(int(i), j, take_right(df1,df2,j,i), allow_duplicates=False)
# print(df)
return df
This won't add new columns or rows to an empty DataFrame.
Thank you!!
One approach is to create an empty output dataframe with the union of columns and indices from df1 and df2 and then use the df.update method to assign their values into the out_df
import pandas as pd
df1 = pd.DataFrame({'A':[1,2], 'B':[3,4]}, index = [0,1])
df2 = pd.DataFrame({'B':[8,9], 'C':[10,11]}, index = [1,2])
out_df = pd.DataFrame(
columns = df1.columns.union(df2.columns),
index = df1.index.union(df2.index),
)
out_df.update(df1)
out_df.update(df2)
out_df
Why does combine_first not work?
df = df2.combine_first(df1)
print(df)
Output:
A B C
0 1.0 3 NaN
1 2.0 8 10.0
2 NaN 9 11.0
Related
I have 3 dataframes like as shown below
ID,col1,col2
1,X,35
2,X,37
3,nan,32
4,nan,34
5,X,21
df1 = pd.read_clipboard(sep=',',skipinitialspace=True)
ID,col1,col2
1,nan,305
2,X,307
3,X,302
4,nan,304
5,X,201
df2 = pd.read_clipboard(sep=',',skipinitialspace=True)
ID,col1,col2
1,X,315
2,nan,317
3,X,312
4,nan,314
5,X,21
df3 = pd.read_clipboard(sep=',',skipinitialspace=True)
Now I want to identify the IDs where col1 is NA in all 3 input dataframes.
So, I tried the below
L1=df1[df1['col1'].isna()]['ID'].tolist()
L2=df2[df2['col1'].isna()]['ID'].tolist()
L3=df3[df3['col1'].isna()]['ID'].tolist()
common_ids_all = list(set.intersection(*map(set, [L1,L2,L3])))
final_df = pd.concat([df1,df2,df3],ignore_index=True)
final_df[final_df['ID'].isin(common_ids_all)]
While the above works, is there any efficient and elegant approach do the above?
As you can see that am repeating the same statement thrice (for 3 dataframes)
However, in my real data, I have 12 dataframes where I have to get IDs where col1 is NA in all 12 dataframes.
update - my current read operation looks like below
fnames = ['file1.xlsx','file2.xlsx', 'file3.xlsx']
dfs=[]
NA_list=[]
def preprocessing(fname):
df= pd.read_excel(fname, sheet_name="Sheet1")
df.columns = df.iloc[7]
df = df.iloc[8: , :]
NA_list.append(df[df['col1'].isna()]['ID'])
dfs.append(df)
[preprocessing(fname) for fname in fnames]
final_df = pd.concat(dfs, ignore_index=True)
L1 = NA_list[0]
L2 = NA_list[1]
L3 = NA_list[2]
final_list = (list(set.intersection(*map(set, [L1,L2,L3]))))
final_df[final_df['ID'].isin(final_list)]
You can use:
dfs = [df1, df2, df3]
final_df = pd.concat(dfs).query('col1.isna()')
final_df = final_df[final_df.groupby('ID')['ID'].transform('size') == len(dfs)]
print(final_df)
# Output
ID col1 col2
3 4 NaN 34
3 4 NaN 304
3 4 NaN 314
Full code:
fnames = ['file1.xlsx','file2.xlsx', 'file3.xlsx']
def preprocessing(fname):
return pd.read_excel(fname, sheet_name='Sheet1', skiprows=6)
dfs = [preprocessing(fname) for fname in fnames]
final_df = pd.concat([df[df['col1'].isna()] for df in dfs])
final_df = final_df[final_df.groupby('ID')['ID'].transform('size') == len(dfs)]
This are times when def function get you sorted. If the dataframe list will continually change I will create a def function. If I got you right the following will do;
def CombinedNaNs(lst):
newdflist =[]
for d in dflist:
newdflist.append(d[d['col1'].isna()])
s=pd.concat(newdflist)
return s[s.duplicated(subset=['ID'], keep=False)].drop_duplicates()
dflist=[df1,df2,df3]#List of dfs
CombinedNaNs(dflist)#apply function
ID col1 col2
3 4 NaN 34
3 4 NaN 304
3 4 NaN 314
How do I add a merge columns of Pandas dataframe to another dataframe while the new columns of data has less rows? Specifically I need to new column of data to be filled with NaN at the first few rows in the merged DataFrame instead of the last few rows. Please refer to the picture. Thanks.
Use:
df1 = pd.DataFrame({
'A':list('abcdef'),
'B':[4,5,4,5,5,4],
})
df2 = pd.DataFrame({
'SMA':list('rty')
})
df3 = df1.join(df2.set_index(df1.index[-len(df2):]))
Or:
df3 = pd.concat([df1, df2.set_index(df1.index[-len(df2):])], axis=1)
print (df3)
A B SMA
0 a 4 NaN
1 b 5 NaN
2 c 4 NaN
3 d 5 r
4 e 5 t
5 f 4 y
How it working:
First is selected index in df1 by length of df2 from back:
print (df1.index[-len(df2):])
RangeIndex(start=3, stop=6, step=1)
And then is overwrite existing values by DataFrame.set_index:
print (df2.set_index(df1.index[-len(df2):]))
SMA
3 r
4 t
5 y
I created a dataframe df = pd.DataFrame({'col':[1,2,3,4,5,6]}) and I would like to take some values and put them in another dataframe df2 = pd.DataFrame({'A':[0,0]})by creating new columns.
I created a new column 'B' df2['B'] = df.iloc[0:2,0] and everything was fine, but then i created another column C df2['C'] = df.iloc[2:4,0] and there were only NaN values. I don't know why and if I print print(df.iloc[2:4]) everything is normal.
full code:
import pandas as pd
df = pd.DataFrame({'col':[1,2,3,4,5,6]})
df2 = pd.DataFrame({'A':[0,0]})
df2['B'] = df.iloc[0:2,0]
df2['C'] = df.iloc[2:4,0]
print(df2)
print('\n',df.iloc[2:4])
output:
A B C
0 0 1 NaN
1 0 2 NaN
col
2 3
3 4
Assignement df2['C'] = df.iloc[2:4,0] does not work as expected, because index is not the same. You can skip this using .values attributes.
import pandas as pd
df = pd.DataFrame({'col':[1,2,3,4,5,6]})
df2 = pd.DataFrame({'A':[0,0]})
df2['B'] = df.iloc[0:2,0]
df2['C'] = df.iloc[2:4,0].values
print(df2)
I have many DataFrames that I need to merge.
Let's say:
base: id constraint
1 'a'
2 'b'
3 'c'
df_1: id value constraint
1 1 'a'
2 2 'a'
3 3 'a'
df_2: id value constraint
1 1 'b'
2 2 'b'
3 3 'b'
df_3: id value constraint
1 1 'c'
2 2 'c'
3 3 'c'
If I try and merge all of them (it'll be in a loop), I get:
a = pd.merge(base, df_1, on=['id', 'constraint'], how='left')
b = pd.merge(a, df_2, on=['id', 'constraint'], how='left')
c = pd.merge(b, df_3, on=['id', 'constraint'], how='left')
id constraint value value_x value_y
1 'a' 1 NaN NaN
2 'b' NaN 2 NaN
3 'c' NaN NaN 3
The desired output would be:
id constraint value
1 'a' 1
2 'b' 2
3 'c' 3
I know about the combine_first and it works, but I can't have this approach because it is thousands of time slower.
Is there a merge that can replace values in case of columns overlap?
It's somewhat similar to this question, with no answers.
Given your MCVE:
import pandas as pd
base = pd.DataFrame([1,2,3], columns=['id'])
df1 = pd.DataFrame([[1,1]], columns=['id', 'value'])
df2 = pd.DataFrame([[2,2]], columns=['id', 'value'])
df3 = pd.DataFrame([[3,3]], columns=['id', 'value'])
I would suggest to concat first your dataframe (using a loop if needed):
df = pd.concat([df1, df2, df3])
And then merge:
pd.merge(base, df, on='id')
It yields:
id value
0 1 1
1 2 2
2 3 3
Update
Runing the code with the new version of your question and the input provided by #Celius Stingher:
a = {'id':[1,2,3],'constrains':['a','b','c']}
b = {'id':[1,2,3],'value':[1,2,3],'constrains':['a','a','a']}
c = {'id':[1,2,3],'value':[1,2,3],'constrains':['b','b','b']}
d = {'id':[1,2,3],'value':[1,2,3],'constrains':['c','c','c']}
base = pd.DataFrame(a)
df1 = pd.DataFrame(b)
df2 = pd.DataFrame(c)
df3 = pd.DataFrame(d)
We get:
id constrains value
0 1 a 1
1 2 b 2
2 3 c 3
Which seems to be compliant with your expected output.
You can use ffill() for the purpose:
df_1 = pd.DataFrame({'val':[1]}, index=[1])
df_2 = pd.DataFrame({'val':[2]}, index=[2])
df_3 = pd.DataFrame({'val':[3]}, index=[3])
(pd.concat((df_1,df_2,df_3), axis=1)
.ffill(1)
.iloc[:,-1]
)
Output:
1 1.0
2 2.0
3 3.0
Name: val, dtype: float64
For your new data:
base.merge(pd.concat((df1,df2,df3)),
on=['id','constraint'],
how='left')
output:
id constraint value
0 1 'a' 1
1 2 'b' 2
2 3 'c' 3
Conclusion: you are actually looking for the option how='left' in merge
If you must only merge all dataframes with base:
Based on edit
import pandas as pd
a = {'id':[1,2,3],'constrains':['a','b','c']}
b = {'id':[1,2,3],'value':[1,2,3],'constrains':['a','a','a']}
c = {'id':[1,2,3],'value':[1,2,3],'constrains':['b','b','b']}
d = {'id':[1,2,3],'value':[1,2,3],'constrains':['c','c','c']}
base = pd.DataFrame(a)
df_1 = pd.DataFrame(b)
df_2 = pd.DataFrame(c)
df_3 = pd.DataFrame(d)
dataframes = [df_1,df_2,df_3]
for i in dataframes:
base = base.merge(i,how='left',on=['id','constrains'])
summation = [col for col in base if col.startswith('value')]
base['value'] = base[summation].sum(axis=1)
base = base.dropna(how='any',axis=1)
print(base)
Output:
id constrains value
0 1 a 1.0
1 2 b 2.0
2 3 c 3.0
For those who want to simply do a merge, overriding the values (which is my case), can achieve that using this method, which is really similar to Celius Stingher answer.
Documented version is on the original gist.
import pandas as pa
def rmerge(left,right,**kwargs):
# Function to flatten lists from http://rosettacode.org/wiki/Flatten_a_list#Python
def flatten(lst):
return sum( ([x] if not isinstance(x, list) else flatten(x) for x in lst), [] )
# Set default for removing overlapping columns in "left" to be true
myargs = {'replace':'left'}
myargs.update(kwargs)
# Remove the replace key from the argument dict to be sent to
# pandas merge command
kwargs = {k:v for k,v in myargs.items() if k is not 'replace'}
if myargs['replace'] is not None:
# Generate a list of overlapping column names not associated with the join
skipcols = set(flatten([v for k, v in myargs.items() if k in ['on','left_on','right_on']]))
leftcols = set(left.columns)
rightcols = set(right.columns)
dropcols = list((leftcols & rightcols).difference(skipcols))
# Remove the overlapping column names from the appropriate DataFrame
if myargs['replace'].lower() == 'left':
left = left.copy().drop(dropcols,axis=1)
elif myargs['replace'].lower() == 'right':
right = right.copy().drop(dropcols,axis=1)
df = pa.merge(left,right,**kwargs)
return df
I want to merge 3 columns into a single column. I have tried changing the column types. However, I could not do it.
For example, I have 3 columns such as A: {1,2,4}, B:{3,4,4}, C:{1,1,1}
Output expected: ABC Column {131, 241, 441}
My inputs are like this:
df['ABC'] = df['A'].map(str) + df['B'].map(str) + df['C'].map(str)
df.head()
ABC {13.01.0 , 24.01.0, 44.01.0}
The type of ABC seems object and I could not change via str, int.
df['ABC'].apply(str)
Also, I realized that there are NaN values in A, B, C column. Is it possible to merge these even with NaN values?
# Example
import pandas as pd
import numpy as np
df = pd.DataFrame()
# Considering NaN's in the data-frame
df['colA'] = [1,2,4, np.NaN,5]
df['colB'] = [3,4,4,3,np.NaN]
df['colC'] = [1,1,1,4,1]
# Using pd.isna() to check for NaN values in the columns
df['colA'] = df['colA'].apply(lambda x: x if pd.isna(x) else str(int(x)))
df['colB'] = df['colB'].apply(lambda x: x if pd.isna(x) else str(int(x)))
df['colC'] = df['colC'].apply(lambda x: x if pd.isna(x) else str(int(x)))
# Filling the NaN values with a blank space
df = df.fillna('')
# Transform columns into string
df = df.astype(str)
# Concatenating all together
df['ABC'] = df.sum(axis=1)
A workaround your NaN problem could look like this but now NaN will be 0
import numpy as np
df = pd.DataFrame({'A': [1,2,4, np.nan], 'B':[3,4,4,4], 'C':[1,np.nan,1, 3]})
df = df.replace(np.nan, 0, regex=True).astype(int).applymap(str)
df['ABC'] = df['A'] + df['B'] + df['C']
output
A B C ABC
0 1 3 1 131
1 2 4 0 240
2 4 4 1 441
3 0 4 3 043