I want to replace df2 elements with df1 elements but according to that: If df2 first row first column has value '1' than df1 first row first column element is getting there, If it is zero than '0' stands. If df2 any row last column element is '1' than df1 that row last column element is coming there. It is going to be like that.
So i want to replace all df2 '1' element with df1 elements according to that rule. df3 is going to be like:
abcde0000;
abcd0e000;
abcd00e00;...
We can use apply function for this. But first you have concat both frames along axis 1. I am using a dummy table with just three entries. It can be applied for any number of rows.
import pandas as pd
import numpy as np
# Dummy data
df1 = pd.DataFrame([['a','b','c','d','e'],['a','b','c','d','e'],['a','b','c','d','e']])
df2 = pd.DataFrame([[1,1,1,1,1,0,0,0,0],[1,1,1,1,0,1,0,0,0],[1,1,1,1,0,0,1,0,0]])
# Display dataframe . May not work in python scripts. I used them in jupyter notebooks
display(df1)
display(df2)
# Concat DFs
df3 = pd.concat([df1,df2],axis=1)
display(df3)
# Define function for replacing
def replace(letters,indexes):
seek =0
for i in range(len(indexes)):
if indexes[i]==1:
indexes[i]=letters[seek]
seek+=1
return ''.join(list(map(str,indexes)))
# Applying replace function to dataframe
df4 = df3.apply(lambda x: replace(x[:5],x[5:]),axis=1)
# Display df4
display(df4)
The result is
0 abcde0000
1 abcd0e000
2 abcd00e00
dtype: object
I think this will solve your problem
Related
I have two data frames:
df1 = pd.read_excel("test1.xlsx")
df2 = pd.read_excel("test2.xlsx")
I am trying to assign values of df1 to df2 where a certain condition is met (Column1 is equal to Column1 then assign values of ColY to ColX).
df1.loc[df1['Col1'] == df2['Col1'],'ColX'] = df2['ColY']
This results in an error as df2['ColY] is the whole column. How do i assign for only the rows that match?
You can use numpy.where:
import numpy as np
df1['ColX'] = np.where(df1['Col1'].eq(df2['Col1']), df2['ColY'], df1['ColX'])
Since you wanted to assign from df1 to df2 your code should have been
df2.loc[df1['Col1']==df2['Col2'],'ColX']=df1.['ColY']
The code you wrote won't assign the values from df1 to df2, but from df2 to df1.
And also if you could clarify to which dataframe ColX and ColY belong to I could help more(Or does both dataframe have them??).
Your code is pretty much right!!! Only change the df1 and df2 as above.
Having a bit of trouble understanding the documentation
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
dfbreed['x'] = dfbreed.apply(testbreed, axis=1)
C:/Users/erasmuss/PycharmProjects/Sarah/farmdata.py:38: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
Code is basically to re-arrange and clean some data to make analysis easier.
Code in given row-by per each animal, but has repetitions, blanks, and some other sparse values
Idea is to basically stack rows into columns and grab the useful data (Weight by date and final BCS) per animal
Initial DF
few snippets of the dataframe
Output Format
Output DF/csv
import pandas as pd
import numpy as np
#Function for cleaning up multiple entries of breeds
def testbreed(x):
if x.first_valid_index() is None:
return None
else:
return x[x.first_valid_index()]
#Read Data
df1 = pd.read_csv("farmdata.csv")
#Drop empty rows
df1.dropna(how='all', axis=1, inplace=True)
#Copy to extract Weights in DF2
df2 = df1.copy()
df2 = df2.drop(['BCS', 'Breed','Age'], axis=1)
#Pivot for ID names in DF1
df1 = df1.pivot(index='ID', columns='Date', values=['Breed','Weight', 'BCS'])
#Pivot for weights in DF2
df2 = df2.pivot(index='ID', columns='Date', values = 'Weight')
#Split out Breeds and BCS into individual dataframes w/Duplicate/missing data for each ID
df3 = df1.copy()
dfbreed = df3[['Breed']]
dfBCS = df3[['BCS']]
#Drop empty BCS columns
df1.dropna(how='all', axis=1, inplace=True)
#Shorten Breed and BCS to single Column by grabbing first value that is real. see function above
dfbreed['x'] = dfbreed.apply(testbreed, axis=1)
dfBCS['x'] = dfBCS.apply(testbreed, axis=1)
#Populate BCS and Breed into new DF
df5= pd.DataFrame(data=None)
df5['Breed'] = dfbreed['x']
df5['BCS'] = dfBCS['x']
#Join Weights
df5 = df5.join(df2)
#Write output
df5.to_csv(r'.\out1.csv')
I want to take the BCS and Breed dataframes which are multi-indexed on the column by Breed or BCS and then by date to take the first non-NaN value in the rows of dates and set that into a column named breed.
I had a lot of trouble getting the columns to pick the first unique values in-situ on the DF
I found a work-around with a 2015 answer:
2015 Answer
which defined the function at the top.
reading through the setting a value on the copy-of a slice makes sense intuitively,
but I can't seem to think of a way to make it work as a direct-replacement or index-based.
Should I be looping through?
Trying from The second answer here
I get
dfbreed.loc[:,'Breed'] = dfbreed['Breed'].apply(testbreed, axis=1)
dfBCS.loc[:, 'BCS'] = dfBCS.apply['BCS'](testbreed, axis=1)
which returns
ValueError: Must have equal len keys and value when setting with an iterable
I'm thinking this has something to do with the multi-index
keys come up as:
MultiIndex([('Breed', '1/28/2021'),
('Breed', '2/12/2021'),
('Breed', '2/4/2021'),
('Breed', '3/18/2021'),
('Breed', '7/30/2021')],
names=[None, 'Date'])
MultiIndex([('BCS', '1/28/2021'),
('BCS', '2/12/2021'),
('BCS', '2/4/2021'),
('BCS', '3/18/2021'),
('BCS', '7/30/2021')],
names=[None, 'Date'])
Sorry for the long question(s?)
Can anyone help me out?
Thanks.
You created dfbreed as:
dfbreed = df3[['Breed']]
So it is a view of the original DataFrame (limited to just this one column).
Remember that a view has not any own data buffer, it is only a tool to "view"
a fragment of the original DataFrame, with read only access.
When you attempt to perform dfbreed['x'] = dfbreed.apply(...), you
actually attempt to violate the read-only access mode.
To avoid this error, create dfbreed as an "independent" DataFrame:
dfbreed = df3[['Breed']].copy()
Now dfbreed has its own data buffer and you are free to change the data.
I have pandas df which has 7000 rows * 7 columns. And I have list (row_list) that consists with the value that I want to filter out from df.
What I want to do is to filter out the rows if the rows from df contain the corresponding value in the list.
This is what I got when I tried,
"Empty DataFrame
Columns: [A,B,C,D,E,F,G]
Index: []"
df = pd.read_csv('filename.csv')
df1 = pd.read_csv('filename1.csv', names = 'A')
row_list = []
for index, rows in df1.iterrows():
my_list = [rows.A]
row_list.append(my_list)
boolean_series = df.D.isin(row_list)
filtered_df = df[boolean_series]
print(filtered_df)
replace
boolean_series = df.RightInsoleImage.isin(row_list)
with
boolean_series = df.RightInsoleImage.isin(df1.A)
And let us know the result. If it doesn't work show a sample of df and df1.A
(1) generating separate dfs for each condition, concat, then dedup (slow)
(2) a custom function to annotate with bool column (default as False, then annotated True if condition is fulfilled), then filter based on that column
(3) keep a list of indices of all rows with your row_list values, then filter using iloc based on your indices list
Without an MRE, sample data, or a reason why your method didn't work, it's difficult to provide a more specific answer.
I have two data frames named df1 & df2:
df1
df2
SITE_NUMBER is set as the index of df1. Now I want to return the 'FACILITY' column of df1 if the values of 'S_STATION' of df2 matches with the index value of df1. I wrote a function for this purpose:
def return_facility():
return df1[df1['SITE_NUMBER'] == pd.to_numeric(df2['S_STATION'])]['FACILITY']
However, the function is not working because compiler does not recognize df1['SITE_NUMBER']. Could anyone point out where I am making mistake?
You need isin. Use it on df1.index
def return_facility():
return df1.loc[df1.index.isin(pd.to_numeric(df2['S_STATION'])), 'FACILITY']
I have 2 dataframes. df1 comprises a Series of values.
df1 = pd.DataFrame({'winnings': cumsums_winnings_s, 'returns':cumsums_returns_s, 'spent': cumsums_spent_s, 'runs': cumsums_runs_s, 'wins': cumsums_wins_s, 'expected': cumsums_expected_s}, columns=["winnings", "returns", "runs", "wins", "expected"])
df2 runs each row through a function which takes 3 columns and produces a result for each row - specialSauce
df2= pd.DataFrame(list(map(lambda w,r,e: doStuff(w,r,e), df1['wins'], df1['runs'], df1['expected'])), columns=["specialSauce"])
print(df2.append(df1))
produces all the df1 columns but NaN for the df1 (and vice versa if df1/df2 switched in append)
So the problem I has is how to append these 2 dataframes correctly.
As I understand things, your issue seems to be related to the fact that you get NaN's in the result DataFrame.
The reason for this is that you are trying to .append() one dataframe to the other while they don't have the same columns.
df2 has one extra column, the one created with apply() and doStuff, while df1 does not have that column. When trying to append one pd.DataFrame to the other the result will have all columns both pd.DataFrame objects. Naturally, you will have some NaN's for ['specialSauce'] since this column does not exist in df1.
This would be the same if you were to use pd.concat(), both methods do the same thing in this case. The one thing that you could do to bring the result closer to your desired result is use the ignore_index flag like this:
>> df2.append(df1, ignore_index=True)
This would at least give you a 'fresh' index for the result pd.DataFrame.
EDIT
If what you're looking for is to "append" the result of doStuff to the end of your existing df, in the form of a new column (['specialSauce']), then what you'll have to do is use pd.concat() like this:
>> pd.concat([df1, df2], axis=1)
This will return the result pd.DataFrame as you want it.
If you had a pd.Series to add to the columns of df1 then you'd need to add it like this:
>> df1['specialSauce'] = <'specialSauce values'>
I hope that helps, if not please rephrase the description of what you're after.
Ok, there are a couple of things going on here. You've left code out and I had to fill in the gaps. For example you did not define doStuff, so I had to.
doStuff = lambda w, r, e: w + r + e
With that defined, your code does not run. I had to guess what you were trying to do. I'm guessing that you want to have an additional column called 'specialSauce' adjacent to your other columns.
So, this is how I set it up and solved the problem.
Setup and Solution
import pandas as pd
import numpy as np
np.random.seed(314)
df = pd.DataFrame(np.random.randn(100, 6),
columns=["winnings", "returns",
"spent", "runs",
"wins", "expected"]).cumsum()
doStuff = lambda w, r, e: w + r + e
df['specialSauce'] = df[['wins', 'runs', 'expected']].apply(lambda x: doStuff(*x), axis=1)
print df.head()
winnings returns spent runs wins expected specialSauce
0 0.166085 0.781964 0.852285 -0.707071 -0.931657 0.886661 -0.752067
1 -0.055704 1.163688 0.079710 0.155916 -1.212917 -0.045265 -1.102266
2 -0.554241 1.928014 0.271214 -0.462848 0.452802 1.692924 1.682878
3 0.627985 3.047389 -1.594841 -1.099262 -0.308115 4.356977 2.949601
4 0.796156 3.228755 -0.273482 -0.661442 -0.111355 2.827409 2.054611
Also
You tried to use pd.DataFrame.append(). Per the linked documentation, it attaches the DataFrame specified as the argument to the end of the DataFrame that is being appended to. You would have wanted to use pd.DataFrame.concat().