Develop Reference

How to split a dictionary of df in half using pandas?

I have a very large dictionary of dataframes. It contains around 250 dataframes, each of which has around 50 columns per df. My goal is to concat the dataframes to create one large df; however, as you can imagine, this process isn't great because it will create a df that is way too large view outside of using python.
My goal is to explode the large dictionary of df in half and turn it into two large, but manageable files.
I will try to replicate what it looks like:
d = {df1, df2,........,df500}
df = pd.concat(d)
# However, Is there a way to split 50%?
df1 = pd.concat(d) # only gets first 250 of the df
df2 =pd.concat(d) # only gets last 250 df

How about something like this?
v = list(d.values())
part1 = v[:len(v)//2]
part2 = v[len(part1):]
df1 = pd.concat(part1)
df2 = pd.concat(part2)

First of all it's not a dictionary , it's a set which can be converted to list.
An List can be divided into 2 as you need.
d=list(d)
ln=len(d)
d1=d[0:ln//2]
d2=d[ln//2:]
df1 = pd.concat(d1)
df2 = pd.concat(d2)

Pandas "A value is trying to be set on a copy of a slice from a DataFrame"

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.

Concatenate two dataframes with different row indices

I want to concatenate two data frames of the same length, by adding a column to the first one (df).
But because certain df rows are being filtered, it seems the index isn't matching.
import pandas as pd
pd.read_csv(io.StringIO(uploaded['customer.csv'].decode('utf-8')), sep=";")
df["Margin"] = df["Sales"]-df["Cost"]
df = df.loc[df["Margin"]>-100000]
df = df.loc[df["Sales"]> 1000]
df.reindex()
df
This returns:
So this operation:
customerCluster = pd.concat([df, clusters], axis = 1, ignore_index= True)
print(customerCluster)
Is returning:
So, I've tried reindex and the argument ignore_index = True as you can see in above code snippet.

Thanks for all the answers. If anyone encounters the same problem, the solution I found was this:
customerID = df["CustomerID"]
customerID = customerID.reset_index(drop=True)
df = df.reset_index(drop=True)
So, basically, the indexes of both data frames are now matching, thus:
customerCluster = pd.concat((customerID, clusters), axis = 1)
This will concatenate correctly the two data frames.

Pandas groupby(df.index) with indexes of varying size

I have an array of dataframes dfs = [df0, df1, ...]. Each one of them have a date column of varying size (some dates might be in one dataframe but not the other).
What I'm trying to do is this:
pd.concat(dfs).groupby("date", as_index=False).sum()
But with date no longer being a column but an index (dfs = [df.set_index("date") for df in dfs]).
I've seen you can pass df.index to groupby (.groupby(df.index)) but df.index might not include all the dates.
How can I do this?
The goal here is to call .sum() on the groupby, so I'm not tied to using groupby nor concat is there's any alternative method to do so.

If I am able to understand maybe you want something like this:
df = pd.concat([dfs])
df.groupby(df.index).sum()
Here's small example:
tmp1 = pd.DataFrame({'date':['2019-09-01','2019-09-02','2019-09-03'],'value':[1,1,1]}).set_index('date')
tmp2 = pd.DataFrame({'date':['2019-09-01','2019-09-02','2019-09-04','2019-09-05'],'value':[2,2,2,2]}).set_index('date')
df = pd.concat([tmp1,tmp2])
df.groupby(df.index).sum()

Unexpected transformation in pandas DataFrame while editing its copy

I have pandas DataFrame df with different types of columns, some values of df are NaN.
To test some assumption, I create copy of df, and transform copied df to (0, 1) with pandas.isnull():
df_copy = df
for column in df_copy:
df_copy[column] = df_copy[column].isnull().astype(int)
but after that BOTH df and df_copy consist of 0 and 1.
Why this code transforms df to 0, 1 and is there way to prevent it?

You can prevent it declaring:
df_copy = df.copy()
This creates a new object. Prior to that you essentially had two pointers to the same object. You also might want to check this answer and note that DataFrames are mutable.
Btw, you could obtain the desired result simply by:
df_copy = df.isnull().astype(int)

even better memory-wise
for column in df:
df[column + 'flag'] = df[column].isnull().astype(int)