The question
Given a Series s and DataFrame df, how do I operate on each column of df with s?
df = pd.DataFrame(
[[1, 2, 3], [4, 5, 6]],
index=[0, 1],
columns=['a', 'b', 'c']
)
s = pd.Series([3, 14], index=[0, 1])
When I attempt to add them, I get all np.nan
df + s
a b c 0 1
0 NaN NaN NaN NaN NaN
1 NaN NaN NaN NaN NaN
What I thought I should get is
a b c
0 4 5 6
1 18 19 20
Objective and motivation
I've seen this kind of question several times over and have seen many other questions that involve some element of this. Most recently, I had to spend a bit of time explaining this concept in comments while looking for an appropriate canonical Q&A. I did not find one and so I thought I'd write one.
These questions usually arises with respect to a specific operation, but equally applies to most arithmetic operations.
How do I subtract a Series from every column in a DataFrame?
How do I add a Series from every column in a DataFrame?
How do I multiply a Series from every column in a DataFrame?
How do I divide a Series from every column in a DataFrame?
It is helpful to create a mental model of what Series and DataFrame objects are.
Anatomy of a Series
A Series should be thought of as an enhanced dictionary. This isn't always a perfect analogy, but we'll start here. Also, there are other analogies that you can make, but I am targeting a dictionary in order to demonstrate the purpose of this post.
index
These are the keys that we can reference to get at the corresponding values. When the elements of the index are unique, the comparison to a dictionary becomes very close.
values
These are the corresponding values that are keyed by the index.
Anatomy of a DataFrame
A DataFrame should be thought of as a dictionary of Series or a Series of Series. In this case the keys are the column names and the values are the columns themselves as Series objects. Each Series agrees to share the same index which is the index of the DataFrame.
columns
These are the keys that we can reference to get at the corresponding Series.
index
This the the index that all of the Series values agree to share.
Note: RE: columns and index objects
They are the same kind of things. A DataFrames index can be used as another DataFrames columns. In fact, this happens when you do df.T to get a transpose.
values
This is a two-dimensional array that contains the data in a DataFrame. The reality is that values is not what is stored inside the DataFrame object. (Well, sometimes it is, but I'm not about to try to describe the block manager). The point is, it is better to think of this as access to a two-dimensional array of the data.
Define Sample Data
These are sample pandas.Index objects that can be used as the index of a Series or DataFrame or can be used as the columns of a DataFrame:
idx_lower = pd.Index([*'abcde'], name='lower')
idx_range = pd.RangeIndex(5, name='range')
These are sample pandas.Series objects that use the pandas.Index objects above:
s0 = pd.Series(range(10, 15), idx_lower)
s1 = pd.Series(range(30, 40, 2), idx_lower)
s2 = pd.Series(range(50, 10, -8), idx_range)
These are sample pandas.DataFrame objects that use the pandas.Index objects above:
df0 = pd.DataFrame(100, index=idx_range, columns=idx_lower)
df1 = pd.DataFrame(
np.arange(np.product(df0.shape)).reshape(df0.shape),
index=idx_range, columns=idx_lower
)
Series on Series
When operating on two Series, the alignment is obvious. You align the index of one Series with the index of the other.
s1 + s0
lower
a 40
b 43
c 46
d 49
e 52
dtype: int64
Which is the same as when I randomly shuffle one before I operate. The indices will still align.
s1 + s0.sample(frac=1)
lower
a 40
b 43
c 46
d 49
e 52
dtype: int64
And is not the case when instead I operate with the values of the shuffled Series. In this case, Pandas doesn't have the index to align with and therefore operates from a positions.
s1 + s0.sample(frac=1).values
lower
a 42
b 42
c 47
d 50
e 49
dtype: int64
Add a scalar
s1 + 1
lower
a 31
b 33
c 35
d 37
e 39
dtype: int64
DataFrame on DataFrame
The similar is true when operating between two DataFrames. The alignment is obvious and does what we think it should do:
df0 + df1
lower a b c d e
range
0 100 101 102 103 104
1 105 106 107 108 109
2 110 111 112 113 114
3 115 116 117 118 119
4 120 121 122 123 124
It shuffles the second DataFrame on both axes. The index and columns will still align and give us the same thing.
df0 + df1.sample(frac=1).sample(frac=1, axis=1)
lower a b c d e
range
0 100 101 102 103 104
1 105 106 107 108 109
2 110 111 112 113 114
3 115 116 117 118 119
4 120 121 122 123 124
It is the same shuffling, but it adds the array and not the DataFrame. It is no longer aligned and will get different results.
df0 + df1.sample(frac=1).sample(frac=1, axis=1).values
lower a b c d e
range
0 123 124 121 122 120
1 118 119 116 117 115
2 108 109 106 107 105
3 103 104 101 102 100
4 113 114 111 112 110
Add a one-dimensional array. It will align with columns and broadcast across rows.
df0 + [*range(2, df0.shape[1] + 2)]
lower a b c d e
range
0 102 103 104 105 106
1 102 103 104 105 106
2 102 103 104 105 106
3 102 103 104 105 106
4 102 103 104 105 106
Add a scalar. There isn't anything to align with, so broadcasts to everything:
df0 + 1
lower a b c d e
range
0 101 101 101 101 101
1 101 101 101 101 101
2 101 101 101 101 101
3 101 101 101 101 101
4 101 101 101 101 101
DataFrame on Series
If DataFrames are to be thought of as dictionaries of Series and Series are to be thought of as dictionaries of values, then it is natural that when operating between a DataFrame and Series that they should be aligned by their "keys".
s0:
lower a b c d e
10 11 12 13 14
df0:
lower a b c d e
range
0 100 100 100 100 100
1 100 100 100 100 100
2 100 100 100 100 100
3 100 100 100 100 100
4 100 100 100 100 100
And when we operate, the 10 in s0['a'] gets added to the entire column of df0['a']:
df0 + s0
lower a b c d e
range
0 110 111 112 113 114
1 110 111 112 113 114
2 110 111 112 113 114
3 110 111 112 113 114
4 110 111 112 113 114
The heart of the issue and point of the post
What about if I want s2 and df0?
s2: df0:
| lower a b c d e
range | range
0 50 | 0 100 100 100 100 100
1 42 | 1 100 100 100 100 100
2 34 | 2 100 100 100 100 100
3 26 | 3 100 100 100 100 100
4 18 | 4 100 100 100 100 100
When I operate, I get the all np.nan as cited in the question:
df0 + s2
a b c d e 0 1 2 3 4
range
0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
4 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
This does not produce what we wanted, because Pandas is aligning the index of s2 with the columns of df0. The columns of the result includes a union of the index of s2 and the columns of df0.
We could fake it out with a tricky transposition:
(df0.T + s2).T
lower a b c d e
range
0 150 150 150 150 150
1 142 142 142 142 142
2 134 134 134 134 134
3 126 126 126 126 126
4 118 118 118 118 118
But it turns out Pandas has a better solution. There are operation methods that allow us to pass an axis argument to specify the axis to align with.
- sub
+ add
* mul
/ div
** pow
And so the answer is simply:
df0.add(s2, axis='index')
lower a b c d e
range
0 150 150 150 150 150
1 142 142 142 142 142
2 134 134 134 134 134
3 126 126 126 126 126
4 118 118 118 118 118
It turns out axis='index' is synonymous with axis=0.
As is axis='columns' synonymous with axis=1:
df0.add(s2, axis=0)
lower a b c d e
range
0 150 150 150 150 150
1 142 142 142 142 142
2 134 134 134 134 134
3 126 126 126 126 126
4 118 118 118 118 118
The rest of the operations
df0.sub(s2, axis=0)
lower a b c d e
range
0 50 50 50 50 50
1 58 58 58 58 58
2 66 66 66 66 66
3 74 74 74 74 74
4 82 82 82 82 82
df0.mul(s2, axis=0)
lower a b c d e
range
0 5000 5000 5000 5000 5000
1 4200 4200 4200 4200 4200
2 3400 3400 3400 3400 3400
3 2600 2600 2600 2600 2600
4 1800 1800 1800 1800 1800
df0.div(s2, axis=0)
lower a b c d e
range
0 2.000000 2.000000 2.000000 2.000000 2.000000
1 2.380952 2.380952 2.380952 2.380952 2.380952
2 2.941176 2.941176 2.941176 2.941176 2.941176
3 3.846154 3.846154 3.846154 3.846154 3.846154
4 5.555556 5.555556 5.555556 5.555556 5.555556
df0.pow(1 / s2, axis=0)
lower a b c d e
range
0 1.096478 1.096478 1.096478 1.096478 1.096478
1 1.115884 1.115884 1.115884 1.115884 1.115884
2 1.145048 1.145048 1.145048 1.145048 1.145048
3 1.193777 1.193777 1.193777 1.193777 1.193777
4 1.291550 1.291550 1.291550 1.291550 1.291550
It's important to address some higher level concepts first. Since my motivation is to share knowledge and teach, I wanted to make this as clear as possible.
I prefer the method mentioned by piSquared (i.e., df.add(s, axis=0)), but another method uses apply together with lambda to perform an action on each column in the dataframe:
>>>> df.apply(lambda col: col + s)
a b c
0 4 5 6
1 18 19 20
To apply the lambda function to the rows, use axis=1:
>>> df.T.apply(lambda row: row + s, axis=1)
0 1
a 4 18
b 5 19
c 6 20
This method could be useful when the transformation is more complex, e.g.:
df.apply(lambda col: 0.5 * col ** 2 + 2 * s - 3)
Just to add an extra layer from my own experience. It extends what others have done here. This shows how to operate on a Series with a DataFrame that has extra columns that you want to keep the values for. Below is a short demonstration of the process.
import pandas as pd
d = [1.056323, 0.126681,
0.142588, 0.254143,
0.15561, 0.139571,
0.102893, 0.052411]
df = pd.Series(d, index = ['const', '426', '428', '424', '425', '423', '427', '636'])
print(df)
const 1.056323
426 0.126681
428 0.142588
424 0.254143
425 0.155610
423 0.139571
427 0.102893
636 0.052411
d2 = {
'loc': ['D', 'D', 'E', 'E', 'F', 'F', 'G', 'G', 'E', 'D'],
'426': [9, 2, 3, 2, 4, 0, 2, 7, 2, 8],
'428': [2, 4, 1, 0, 2, 1, 3, 0, 7, 8],
'424': [1, 10, 5, 8, 2, 7, 10, 0, 3, 5],
'425': [9, 2, 6, 8, 9, 1, 7, 3, 8, 6],
'423': [4, 2, 8, 7, 9, 6, 10, 5, 9, 9],
'423': [2, 7, 3, 10, 8, 1, 2, 9, 3, 9],
'427': [4, 10, 4, 0, 8, 3, 1, 5, 7, 7],
'636': [10, 5, 6, 4, 0, 5, 1, 1, 4, 8],
'seq': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
}
df2 = pd.DataFrame(d2)
print(df2)
loc 426 428 424 425 423 427 636 seq
0 D 9 2 1 9 2 4 10 1
1 D 2 4 10 2 7 10 5 1
2 E 3 1 5 6 3 4 6 1
3 E 2 0 8 8 10 0 4 1
4 F 4 2 2 9 8 8 0 1
5 F 0 1 7 1 1 3 5 1
6 G 2 3 10 7 2 1 1 1
7 G 7 0 0 3 9 5 1 1
8 E 2 7 3 8 3 7 4 1
9 D 8 8 5 6 9 7 8 1
To multiply a DataFrame by a Series and keep dissimilar columns
Create a list of the elements in the DataFrame and Series you want to operate on:
col = ['426', '428', '424', '425', '423', '427', '636']
Perform your operation using the list and indicate the axis to use:
df2[col] = df2[col].mul(df[col], axis=1)
print(df2)
loc 426 428 424 425 423 427 636 seq
0 D 1.140129 0.285176 0.254143 1.40049 0.279142 0.411572 0.524110 1
1 D 0.253362 0.570352 2.541430 0.31122 0.976997 1.028930 0.262055 1
2 E 0.380043 0.142588 1.270715 0.93366 0.418713 0.411572 0.314466 1
3 E 0.253362 0.000000 2.033144 1.24488 1.395710 0.000000 0.209644 1
4 F 0.506724 0.285176 0.508286 1.40049 1.116568 0.823144 0.000000 1
5 F 0.000000 0.142588 1.779001 0.15561 0.139571 0.308679 0.262055 1
6 G 0.253362 0.427764 2.541430 1.08927 0.279142 0.102893 0.052411 1
7 G 0.886767 0.000000 0.000000 0.46683 1.256139 0.514465 0.052411 1
8 E 0.253362 0.998116 0.762429 1.24488 0.418713 0.720251 0.209644 1
9 D 1.013448 1.140704 1.270715 0.93366 1.256139 0.720251 0.419288 1
Related
I can't get result from column ['a percent'], only column ['b percent'] get filled.
can someone help ? or any reference link will be helpfull ? many thanks
test = pd.DataFrame({'a':[20,2,7,1,7,7,34],'b':[100,99,102,103,56,70,200]})
test['a percent'] = ''
test['b percent'] = ''
x = ['a','b']
for row in test.index:
for x in x:
test.loc[row,f'{x} percent'] = stats.percentileofscore(test[x],test.loc[row,x])/100
output:
a b a percent b percent
0 20 100 0.857143 0.571429
1 2 99 0.428571
2 7 102 0.714286
3 1 103 0.857143
4 7 56 0.142857
5 7 70 0.285714
6 34 200 1.0
You overwrite your x in for x in x, use a different name:
cols = ['a','b']
for row in test.index:
for x in cols:
test.loc[row,f'{x} percent'] = stats.percentileofscore(test[x],test.loc[row,x])/100
That said a better, vectorial method would be:
cols = ['a', 'b']
test.update(test[cols].apply(lambda c: stats.percentileofscore(c, c))
.div(100).add_suffix(' percent'))
Output:
a b a percent b percent
0 20 100 0.857143 0.571429
1 2 99 0.285714 0.428571
2 7 102 0.571429 0.714286
3 1 103 0.142857 0.857143
4 7 56 0.571429 0.142857
5 7 70 0.571429 0.285714
6 34 200 1.0 1.0
I want to create a column C (based on B) which counts each beginning of a series of 4 entries in B (or the dataframe as general). I have the following pandas data frame:
A B
1 100
2 102
3 103
4 104
5 105
6 106
7 108
8 109
9 110
10 112
11 113
12 115
13 116
14 118
15 120
16 121
I want to create the following column C:
A C
1 1
2 1
3 1
4 1
5 2
6 2
7 2
8 2
9 3
10 3
11 3
12 3
13 4
14 4
15 4
16 4
This column C should count each series of 4 entries of the dataframe.
Thanks in advance.
Use:
df['C'] = df.index // 4 + 1
Given that you have fairly simple dataframe it's okay to assume that you have generic index which is a RangeIndex object.
In your example it would look like this:
df.index
#RangeIndex(start=0, stop=16, step=1)
That being said values of this index are the following:
df.index.values
#array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], dtype=int64)
Converting such array into your desired output is performed using the formula:
x // 4 + 1
Where // is the operator used for floor division.
General solution is create numpy array by np.arange, then use integer division by 4 and add 1, because python count from 0:
df['C'] = np.arange(len(df)) // 4 + 1
print (df)
A B C
0 1 100 1
1 2 102 1
2 3 103 1
3 4 104 1
4 5 105 2
5 6 106 2
6 7 108 2
7 8 109 2
8 9 110 3
9 10 112 3
10 11 113 3
11 12 115 3
12 13 116 4
13 14 118 4
14 15 120 4
15 16 121 4
I have the following column with many missing values '?' in store_data dataframe
>>>store_data['trestbps']
0 140
1 130
2 132
3 142
4 110
5 120
6 150
7 180
8 120
9 160
10 126
11 140
12 110
13 ?
I replaced all missing values with -999
store_data.replace('?', -999, inplace = True)
>>>store_data['trestbps']
0 140
1 130
2 132
3 142
4 110
5 120
6 150
7 180
8 120
9 160
10 126
11 140
12 110
13 -999
Now I want to bin the values, I used this code but the output appears all as Nan:
trestbps = store_data['trestbps']
trestbps_bins = [-999,120,140,200]
store_data['trestbps'] = pd.cut(trestbps,trestbps_bins)
>>>store_data['trestbps']
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
5 NaN
6 NaN
7 NaN
8 NaN
9 NaN
10 NaN
11 NaN
12 NaN
13 NaN
The categories work fine when there is no missing values.
I want my output to be categorized from (0-12) and only 13 is replaced by -999. How can I achieve this?
IIUC, you may do:
bins=[0,120,140,200] #set bins
df.trestbps=pd.cut(df.trestbps,bins) #do the cut
df.trestbps=df.trestbps.values.add_categories(999) #add category as 999
df.trestbps.fillna(999) #fillna with 999
0 (120, 140]
1 (120, 140]
2 (120, 140]
3 (140, 200]
4 (0, 120]
5 (0, 120]
6 (140, 200]
7 (140, 200]
8 (0, 120]
9 (140, 200]
10 (120, 140]
11 (120, 140]
12 (0, 120]
13 999
I have a dataframe df:
codeID
0 4829
1 2348
2 401
3 281
4 Z3995
5 O888
6 v71.9
7 NaN
8 Z863 3
9 9
10 50
I wanted to only extract the first 3 digits of the column codeID. If there is any alphabet in front, then I would like to ignore the alphabet. I also wanted to ignore numbers after the decimal. If there is any space between the digits, I wanted to ignore the digits after the space. What i want is the following:
codeID
0 482
1 234
2 401
3 281
4 399
5 888
6 071
7 NaN
8 863
9 009
10 050
I tried to first separate alphabets and numbers using
df[['Let', 'Num']] = df['codeID'].str.extract(r'([A-Za-z]+)([\d\.]+)', expand=True)
but it results in NaN for the rows where there is no alphabet. Could someone suggest me how can i efficiently do this?
You can use extract directly as it only extracts the first occurrence, just add zfill at the end to fill for the missing digits:
result = df.codeID.str.extract(r'(\d{1,3})').squeeze().str.zfill(3)
print(result)
Output
0 482
1 234
2 401
3 281
4 399
5 888
6 071
7 NaN
8 863
9 009
10 050
Name: 0, dtype: object
Note that the pattern above searches for the occurrence of at least 1 digit, as in the case of 9 in your input example, up to 3 digits.
Another way of doing this can using lambda and regex:
%%time
df = pd.DataFrame({'CodeID': ['4829','2348','401','281','Z3995', 'O888','v71.9','NaN', 'Z863 3', '9','50']})
print(df['CodeID'].apply(lambda x: 'NaN' if x=='NaN' else re.findall('[0-9]{1,3}', x)[0]).str.zfill(3))
Output:
0 482
1 234
2 401
3 281
4 399
5 888
6 071
7 NaN
8 863
9 009
10 050
Name: CodeID, dtype: object
Wall time: 0 ns
If there are only alphabets in the word then to avoid any error following code can be used:
1) To print 'NaN' instead of alphabets:
print(df['CodeID'].apply(lambda x: re.findall('[0-9]{1,3}', x)[0] if re.findall('[0-9]{1,3}', x) else 0).str.zfill(3))
Output:
0 482
1 234
2 401
3 281
4 399
5 888
6 071
7 NaN
8 863
9 009
10 050
11 NaN
2) To print '000' instead of alphabets:
print(df['CodeID'].apply(lambda x: re.findall('[0-9]{1,3}', x)[0] if re.findall('[0-9]{1,3}', x) else '0').str.zfill(3))
Output:
0 482
1 234
2 401
3 281
4 399
5 888
6 071
7 000
8 863
9 009
10 050
11 000
Hope this will solve the question!
I did it without a regex, bulkier but still not too bad:
df.loc[~df.codeID.isna(), 'codeID'] = df.codeID.dropna().apply(lambda x: x.split(' ')[0])
df.loc[~df.codeID.isna(), 'codeID'] = df.codeID.dropna().apply(lambda x: x.split('.')[0])
df.codeID = df.codeID.str.lstrip('acdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ')
df.codeID = df.codeID.str.slice(0,3)
df.codeID = df.codeID.str.zfill(3)
df
>
codeID
0 482
1 234
2 401
3 281
4 399
5 888
6 071
7 nan
8 863
9 009
10 050
I have the following, using a DF that has two columns that I would like to aggregate by:
df2.groupby(['airline_clean','sentiment']).size()
airline_clean sentiment
americanair -1 14
0 36
1 1804
2 722
3 171
4 1
jetblue -1 2
0 7
1 1074
2 868
3 250
4 11
southwestair -1 4
0 20
1 1320
2 829
3 237
4 4
united -1 7
0 74
1 2467
2 1026
3 221
4 5
usairways -1 5
0 62
1 1962
2 716
3 155
4 2
virginamerica -1 2
0 2
1 250
2 180
3 69
dtype: int64
Plotting the aggragated view:
dfc=df2.groupby(['airline_clean','sentiment']).size()
dfc.plot(kind='bar', stacked=True,figsize=(18,6))
Results in:
I would like to change two things:
plot the data in a stacked chart by airline
using % instead of raw numbers (by airline as well)
I am not sure how to achieve that. Any direction is appreciated.
The best way is to plot this dataset is to convert to % values first and use unstack() for plotting:
airline_sentiment = df3.groupby(['airline_clean', 'sentiment']).agg({'tweet_count': 'sum'})
airline = df3.groupby(['airline_clean']).agg({'tweet_count': 'sum'})
p = airline_sentiment.div(airline, level='airline_clean') * 100
p.unstack().plot(kind='bar',stacked=True,figsize=(9, 6),title='Sentiment % distribution by airline')
This results in a nice chart: