I have the following dataframe:
ID col_1
1 NaN
2 NaN
3 4.0
2 NaN
2 NaN
3 NaN
3 3.0
1 NaN
I need the following output:
ID col_1
1 NaN
1 NaN
2 NaN
2 NaN
2 NaN
how to do this in pandas
You can create a boolean mask with isna then group this mask by ID and transform using all, then you can filter the rows with the help of this mask:
mask = df['col_1'].isna().groupby(df['ID']).transform('all')
df[mask].sort_values('ID')
Alternatively you can use groupby + filter to filter out the groups which satisfy the condition where all values in col_1 are NaN but this method should be slower than the above:
df.groupby('ID').filter(lambda g: g['col_1'].isna().all()).sort_values('ID')
ID col_1
0 1 NaN
7 1 NaN
1 2 NaN
3 2 NaN
4 2 NaN
Let us try with isin after groupby with all
s = df['col_1'].isna().groupby(df['ID']).all()
df = df.loc[df.ID.isin(s[s].index.tolist())]
df
Out[73]:
ID col_1
0 1 NaN
1 2 NaN
3 2 NaN
4 2 NaN
7 1 NaN
import pandas as pd
import numpy as np
df=pd.read_excel(r"D:\Stack_overflow\test12.xlsx")
df1=(df[df['cols_1'].isnull()]).sort_values(by=['ID'])
I think we can simply take out the null values.
Related
I am new to Pandas. I have a data set with in this format.
UserID ISBN BookRatings
0 276725.0 034545104U 0.0
1 276726.0 155061224 5.0
2 276727.0 446520802 0.0
3 276729.0 052165615W 3.0
4 276729.0 521795028 6.0
I would like to create this
ISBN 276725 276726 276727 276729
UserID
0 034545104U
1 0 155061224 0 0 0
2 0 0 446520802 0 0
3 0 0 0 052165615W 0
4 0 0 0 521795028 0
I tried pivot but was not successful. Any kind advice please?
I think that pivot() is the right approach here. The most difficult part is to get the arguments correctly. I think we need to keep the original index and the new columns should be the values in column UserID. Also, we want to fill the new dataframe with the values from column ISBN.
For this, I firstly extract the original index as column and then apply the pivot() function:
df = df.reset_index()
result = df.pivot(index='index', columns='UserID', values='ISBN')
# Make your float columns to integers (only works if all user ids are numbers, drop nan values first)
result.columns = map(int,result.columns)
Output:
276725 276726 276727 276729
index
0 034545104U NaN NaN NaN
1 NaN 155061224 NaN NaN
2 NaN NaN 446520802 NaN
3 NaN NaN NaN 052165615W
4 NaN NaN NaN 521795028
Edit: If you want the same appearance as in the original dataframe you have to apply the following line as well:
result = result.rename_axis(None, axis=0)
Output:
276725 276726 276727 276729
0 034545104U NaN NaN NaN
1 NaN 155061224 NaN NaN
2 NaN NaN 446520802 NaN
3 NaN NaN NaN 052165615W
4 NaN NaN NaN 521795028
I am new to Pandas and I am stuck at this specific problem where I have 2 DataFrames in Pandas, e.g.
>>> df1
A B
0 1 9
1 2 6
2 3 11
3 4 8
>>> df2
A B
0 Nan 0.05
1 Nan 0.05
2 0.16 Nan
3 0.16 Nan
What I am trying to achieve is to retain all values from df1 except where there is a NaN in df2 i.e.
>>> df3
A B
0 Nan 9
1 Nan 6
2 3 Nan
3 4 Nan
I am talking about dfs with 10,000 rows each so I can't do this manually. Also indices and columns are the exact same in each case. I also have no NaN values in df1.
As far as I understand df.update() will either overwrite all values including NaN or update only those that are NaN.
You can use boolean masking using DataFrame.notna.
# df2 = df2.astype(float) # This needed if your dtypes are not floats.
m = df2.notna()
df1[m]
A B
0 NaN 9.0
1 NaN 6.0
2 3.0 NaN
3 4.0 NaN
This question already has answers here:
Pandas Summing Two Columns with Nan
(2 answers)
Closed 1 year ago.
I'm following up on questions that were asked a few years ago: here and
here
I would like to sum two columns from a pandas data frame where both columns contain missing values.
I have scrolled the internet, but coudn't find this precise output I'm looking for.
I have a df as follows, and I want to sum col1 and col2
col1 col2
1 NaN
NaN 1
1 1
Nan Nan
The output I want:
col1 col2 col_sum
1 NaN 1
NaN 1 1
1 1 2
Nan Nan Nan
What I don't want:
When simply using df['col_sum'] = df['col1'] + df['col2'] gives me
col1 col2 col_sum
1 NaN Nan
NaN 1 Nan
1 1 2
Nan Nan Nan
When using the sum() function as suggested in the above (linked) threads gives me
col1 col2 col_sum
1 NaN 1
NaN 1 1
1 1 2
Nan Nan 0
Hence, I would like that the sum of a number with a missing value outputs that number, and the sum of two missing values outputs a missing value.
Treating Nan's as 0 values is a problem for me. Because later on, if I'm taking the mean() of col_sum having a 0 or a Nan will give a totally different result (or isn't it ??).
Use Series.add with fill_value parameter:
df['col_sum'] = df['col1'].add(df['col2'], fill_value=0)
Or sum with min_count=1 parameter:
df['col_sum'] = df.sum(min_count=1, axis=1)
print (df)
0 1.0 NaN 1.0
1 NaN 1.0 1.0
2 1.0 1.0 2.0
3 NaN NaN NaN
I want to clean some data by replacing only CONSECUTIVE 0s in a data frame
Given:
import pandas as pd
import numpy as np
d = [[1,np.NaN,3,4],[2,0,0,np.NaN],[3,np.NaN,0,0],[4,np.NaN,0,0]]
df = pd.DataFrame(d, columns=['a', 'b', 'c', 'd'])
df
a b c d
0 1 NaN 3 4.0
1 2 0.0 0 NaN
2 3 NaN 0 0.0
3 4 NaN 0 0.0
The desired result should be:
a b c d
0 1 NaN 3 4.0
1 2 0.0 NaN NaN
2 3 NaN NaN NaN
3 4 NaN NaN NaN
where column c & d are affected but column b is NOT affected as it only has 1 zero (and not consecutive 0s).
I have experimented with this answer:
Replacing more than n consecutive values in Pandas DataFrame column
which is along the right lines but the solution keeps the first 0 in a given column which is not desired in my case.
Let us do shift with mask
df=df.mask((df.shift().eq(df)|df.eq(df.shift(-1)))&(df==0))
Out[469]:
a b c d
0 1 NaN 3.0 4.0
1 2 0.0 NaN NaN
2 3 NaN NaN NaN
3 4 NaN NaN NaN
How can I iterarate over rows in a dataframe until the sample ID change?
my_df:
ID loc_start
sample1 10
sample1 15
sample2 10
sample2 20
sample3 5
Something like:
samples = ["sample1", "sample2" ,"sample3"]
out = pd.DataFrame()
for sample in samples:
if my_df["ID"] == sample:
my_list = []
for index, row in my_df.iterrows():
other_list = [row.loc_start]
my_list.append(other_list)
my_list = pd.DataFrame(my_list)
out = pd.merge(out, my_list)
Expected output:
sample1 sample2 sample3
10 10 5
15 20
I realize of course that this could be done easier if my_df really would look like this. However, what I'm after is the principle to iterate over rows until a certain column value change.
Based on the input & output provided, this should work.
You need to provide more info if you are looking for something else.
df.pivot(columns='ID', values = 'loc_start').rename_axis(None, axis=1).apply(lambda x: pd.Series(x.dropna().values))
output
sample1 sample2 sample3
0 10.0 10.0 5.0
1 15.0 20.0 NaN
Ben.T is correct that a pivot works here. Here is an example:
import pandas as pd
import numpy as np
df = pd.DataFrame(data=np.random.randint(0, 5, (10, 2)), columns=list("AB"))
# what does the df look like? Here, I consider column A to be analogous to your "ID" column
In [5]: df
Out[5]:
A B
0 3 1
1 2 1
2 4 2
3 4 1
4 0 4
5 4 2
6 4 1
7 3 1
8 1 1
9 4 0
# now do a pivot and see what it looks like
df2 = df.pivot(columns="A", values="B")
In [8]: df2
Out[8]:
A 0 1 2 3 4
0 NaN NaN NaN 1.0 NaN
1 NaN NaN 1.0 NaN NaN
2 NaN NaN NaN NaN 2.0
3 NaN NaN NaN NaN 1.0
4 4.0 NaN NaN NaN NaN
5 NaN NaN NaN NaN 2.0
6 NaN NaN NaN NaN 1.0
7 NaN NaN NaN 1.0 NaN
8 NaN 1.0 NaN NaN NaN
9 NaN NaN NaN NaN 0.0
Not quite what you wanted. With a little help from Jezreal's answer
df2 = df2.apply(lambda x: pd.Series(x.dropna().values))
In [20]: df3
Out[20]:
A 0 1 2 3 4
0 4.0 1.0 1.0 1.0 2.0
1 NaN NaN NaN 1.0 1.0
2 NaN NaN NaN NaN 2.0
3 NaN NaN NaN NaN 1.0
4 NaN NaN NaN NaN 0.0
The empty spots in the dataframe have to be filled with something, and NaN is used by default. Is this what you wanted?
If, on the other hand, you wanted to perform an operation on your data you would use the groupby instead.
df2 = df.groupby(by="A", as_index=False).mean()