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how do I insert a column at a specific column index in pandas?
(6 answers)
Closed last year.
I have this df:
id car truck bus bike
0 1 1 0 0
1 0 0 1 0
2 1 1 1 1
I want to add another column count to this df but after id and before car to sum the values of the rows, like this:
id count car truck bus bike
0 2 1 1 0 0
1 1 0 0 1 0
2 4 1 1 1 1
I know how to add the column using this code:
df.loc[:,'count'] = df.sum(numeric_only=True, axis=1)
but the above code add the new column in the last position.
How can I fix this?
There are several ways. I provided two ways here.
#1. Changing column order after creating count column:
df.loc[:,'count'] = df.sum(numeric_only=True, axis=1)
df.columns = ['id', 'count', 'car', 'truck', 'bus', 'bike']
print(df)
# id count car truck bus bike
#0 0 2 1 1 0 0
#1 1 2 0 0 1 0
#2 2 6 1 1 1 1
#2. Inserting a Series to specific position using insert function:
df.insert(1, "count", df.sum(numeric_only=True, axis=1))
print(df)
# id count car truck bus bike
#0 0 2 1 1 0 0
#1 1 2 0 0 1 0
#2 2 6 1 1 1 1
try this slight modification of your code:
import pandas as pd
df = pd.DataFrame(data={'id':[0,1,2],'car':[1,0,1],'truck':[1,0,1],'bus':[0,1,1],'bike':[0,0,1]})
count = df.drop(columns=['id'],axis=1).sum(numeric_only=True, axis=1)
df.insert(1, "count", count)
print(df)
Related
df = pd.DataFrame({'id': ['id1', 'id1','id1', 'id2','id1','id1','id1'],
'activity':['swimming','running','jogging','walking','walking','walking','walking'],
'month':[2,3,4,3,4,4,3]})
pd.crosstab(df['id'], df['activity'])
I'd like to add another column for month in the output to get counts per user within each month for the respective activity.
df.set_index(['id','month'])['activity'].unstack().reset_index()
I get error.
edit: Expected output in the image. I do not know how to create a table.
You can pass a list of columns to pd.crosstab:
x = pd.crosstab([df["id"], df["month"]], df["activity"]).reset_index()
x.columns.name = None
print(x)
Prints:
id month jogging running swimming walking
0 id1 2 0 0 1 0
1 id1 3 0 1 0 1
2 id1 4 1 0 0 2
3 id2 3 0 0 0 1
I have data that looks like this:
CHROM POS REF ALT ... is_sever_int is_sever_str is_sever_f encoding_str
0 chr1 14907 A G ... 1 1 one one
1 chr1 14930 A G ... 1 1 one one
These are the columns that I'm interested to perform calculations on (example) :
is_severe snp _id encoding
1 1 one
1 1 two
0 1 one
1 2 two
0 2 two
0 2 one
what I want to do is to count for each snp_id and severe_id how many ones and twos are in the encoding column :
snp_id is_svere encoding_one encoding_two
1 1 1 1
1 0 1 0
2 1 0 1
2 0 1 1
I tried this :
df.groupby(["snp_id","is_sever_f","encoding_str"])["encoding_str"].count()
but it gave the error :
incompatible index of inserted column with frame index
then i tried this:
df["count"]=df.groupby(["snp_id","is_sever_f","encoding_str"],as_index=False)["encoding_str"].count()
and it returned:
Expected a 1D array, got an array with shape (2532831, 3)
how can i fix this? thank you:)
Let's try groupby with whole columns and get size of each group then unstack the encoding index.
out = (df.groupby(['is_severe', 'snp_id', 'encoding']).size()
.unstack(fill_value=0)
.add_prefix('encoding_')
.reset_index())
print(out)
encoding is_severe snp_id encoding_one encoding_two
0 0 1 1 0
1 0 2 1 1
2 1 1 1 1
3 1 2 0 1
Try as follows:
Use pd.get_dummies to convert categorical data in column encoding into indicator variables.
Chain df.groupby and get sum to turn double rows per group into one row (i.e. [0,1] and [1,0] will become [1,1] where df.snp_id == 2 and df.is_severe == 0).
res = pd.get_dummies(data=df, columns=['encoding'])\
.groupby(['snp_id','is_severe'], as_index=False, sort=False).sum()
print(res)
snp_id is_severe encoding_one encoding_two
0 1 1 1 1
1 1 0 1 0
2 2 1 0 1
3 2 0 1 1
If your actual df has more columns, limit the assigment to the data parameter inside get_dummies. I.e. use:
res = pd.get_dummies(data=df[['is_severe', 'snp_id', 'encoding']],
columns=['encoding']).groupby(['snp_id','is_severe'],
as_index=False, sort=False)\
.sum()
i have a df like this:
id
month
1
1
1
3
1
4
1
6
i want to transform it become like this:
id
1
2
3
4
5
6
1
1
0
1
1
0
1
ive tried using this code:
ndf = df[['id']].join(pd.get_dummies(
df['month'])).groupby('id').max()
but it shows like this:
id
1
3
4
6
1
1
1
1
1
how can i insert the middle column (2 and 5) even if it's not in the data?
You can use pd.crosstab
instead, then create new columns using pd.RangeIndex based on the min and max month, and finally use DataFrame.reindex (and optionally DataFrame.reset_index afterwards):
import pandas as pd
new_cols = pd.RangeIndex(df['month'].min(), df['month'].max())
res = (
pd.crosstab(df['id'], df['month'])
.reindex(columns=new_cols, fill_value=0)
.reset_index()
)
Output:
>>> res
id 1 2 3 4 5
0 1 1 0 1 1 0
I have to take this dataframe:
d = {'Apple': [0,0,1,0,1,0], 'Aurora': [0,0,0,0,0,1], 'Barn': [0,1,1,0,0,0]}
df = pd.DataFrame(data=d)
Apple Aurora Barn
0 0 0 0
1 0 0 1
2 1 0 1
3 0 0 0
4 1 0 0
5 0 1 0
And count the frequency of the number one in each column, and create a new dataframe that looks like this:
df = pd.DataFrame([['Apple',0.3333], ['Aurora',0.166666], ['Barn', 0.3333]], columns = ['index', 'value'])
index value
0 Apple 0.333300
1 Aurora 0.166666
2 Barn 0.333300
I have tried this:
df['freq'] = df.groupby(1)[1].transform('count')
But I get an error: KeyError: 1
So I'm not sure how to count the value 1 across rows and columns, and group by column names and the frequency of 1 in each column.
If I understand correctly, you could do simply this:
freq = df.mean()
Output:
>>> freq
Apple 0.333333
Aurora 0.166667
Barn 0.333333
dtype: float64
A conflict row is that two rows have same feature but with different label, like this:
feature label
a 1
a 0
Now, I want to merge these conflict rows to only one label getting from their counts. If I have more a 1, then a will be labeled as 1. Otherwise, a should be labeled as 0.
I can find these conflicts by df1=df.groupy('feature', as_index=Fasle).nunique(),df1 = df1[df1['label]==2]' , and their value counts by df2 = df.groupby("feature")["label"].value_counts().reset_index(name="counts").
But how to find these conflic rows and their counts in one Dataframe (df_conflict = ?), and then merge them by counts, (df_merged = merge(df))?
Lets take df = pd.DataFrame({"feature":['a','a','b','b','a','c','c','d'],'label':[1,0,0,1,1,0,0,1]}) as example.
feature label
0 a 1
1 a 0
2 b 0
3 b 1
4 a 1
5 c 0
6 c 0
7 d 1
df_conflict should be :
feature label counts
a 1 2
a 0 1
b 0 1
b 1 1
And df_merged will be:
feature label
a 1
b 0
c 0
d 1
I think you need first filter groups with count of unique values by DataFrameGroupBy.nunique with GroupBy.transform before SeriesGroupBy.value_counts:
df1 = df[df.groupby('feature')['label'].transform('nunique').gt(1)]
df_conflict = df1.groupby('feature')['label'].value_counts().reset_index(name='count')
print (df_conflict)
feature label count
0 a 1 2
1 a 0 1
2 b 0 1
3 b 1 1
For second get feature with labels by maximum occurencies:
df_merged = df.groupby('feature')['label'].agg(lambda x: x.value_counts().index[0]).reset_index()
print (df_merged)
feature label
0 a 1
1 b 0
2 c 0
3 d 1