%load_ext autoreload
%autoreload 2
from prettypandas import PrettyPandas
import pandas as pd
import numpy as np
np.random.seed(24)
df = pd.DataFrame({'A': np.linspace(1, 10, 10)})
df = pd.concat([df, pd.DataFrame(np.random.randn(10, 4), columns=list('BCDE'))],
axis=1)
df
A | B | C | D | E | |
---|---|---|---|---|---|
0 | 1.0 | 1.329212 | -0.770033 | -0.316280 | -0.990810 |
1 | 2.0 | -1.070816 | -1.438713 | 0.564417 | 0.295722 |
2 | 3.0 | -1.626404 | 0.219565 | 0.678805 | 1.889273 |
3 | 4.0 | 0.961538 | 0.104011 | -0.481165 | 0.850229 |
4 | 5.0 | 1.453425 | 1.057737 | 0.165562 | 0.515018 |
5 | 6.0 | -1.336936 | 0.562861 | 1.392855 | -0.063328 |
6 | 7.0 | 0.121668 | 1.207603 | -0.002040 | 1.627796 |
7 | 8.0 | 0.354493 | 1.037528 | -0.385684 | 0.519818 |
8 | 9.0 | 1.686583 | -1.325963 | 1.428984 | -2.089354 |
9 | 10.0 | -0.129820 | 0.631523 | -0.586538 | 0.290720 |
df.pipe(PrettyPandas).total(axis=None)
A | B | C | D | E | Total | |
---|---|---|---|---|---|---|
0 | 1 | 1.32921 | -0.770033 | -0.31628 | -0.99081 | 0.252088 |
1 | 2 | -1.07082 | -1.43871 | 0.564417 | 0.295722 | 0.350609 |
2 | 3 | -1.6264 | 0.219565 | 0.678805 | 1.88927 | 4.16124 |
3 | 4 | 0.961538 | 0.104011 | -0.481165 | 0.850229 | 5.43461 |
4 | 5 | 1.45342 | 1.05774 | 0.165562 | 0.515018 | 8.19174 |
5 | 6 | -1.33694 | 0.562861 | 1.39285 | -0.063328 | 6.55545 |
6 | 7 | 0.121668 | 1.2076 | -0.00204021 | 1.6278 | 9.95503 |
7 | 8 | 0.354493 | 1.03753 | -0.385684 | 0.519818 | 9.52615 |
8 | 9 | 1.68658 | -1.32596 | 1.42898 | -2.08935 | 8.70025 |
9 | 10 | -0.12982 | 0.631523 | -0.586538 | 0.29072 | 10.2059 |
Total | 55 | 1.74294 | 1.28612 | 2.45891 | 2.84508 |
PrettyPandas(df).total()
A | B | C | D | E | |
---|---|---|---|---|---|
0 | 1 | 1.32921 | -0.770033 | -0.31628 | -0.99081 |
1 | 2 | -1.07082 | -1.43871 | 0.564417 | 0.295722 |
2 | 3 | -1.6264 | 0.219565 | 0.678805 | 1.88927 |
3 | 4 | 0.961538 | 0.104011 | -0.481165 | 0.850229 |
4 | 5 | 1.45342 | 1.05774 | 0.165562 | 0.515018 |
5 | 6 | -1.33694 | 0.562861 | 1.39285 | -0.063328 |
6 | 7 | 0.121668 | 1.2076 | -0.00204021 | 1.6278 |
7 | 8 | 0.354493 | 1.03753 | -0.385684 | 0.519818 |
8 | 9 | 1.68658 | -1.32596 | 1.42898 | -2.08935 |
9 | 10 | -0.12982 | 0.631523 | -0.586538 | 0.29072 |
Total | 55 | 1.74294 | 1.28612 | 2.45891 | 2.84508 |
PrettyPandas(df).average()
A | B | C | D | E | |
---|---|---|---|---|---|
0 | 1 | 1.32921 | -0.770033 | -0.31628 | -0.99081 |
1 | 2 | -1.07082 | -1.43871 | 0.564417 | 0.295722 |
2 | 3 | -1.6264 | 0.219565 | 0.678805 | 1.88927 |
3 | 4 | 0.961538 | 0.104011 | -0.481165 | 0.850229 |
4 | 5 | 1.45342 | 1.05774 | 0.165562 | 0.515018 |
5 | 6 | -1.33694 | 0.562861 | 1.39285 | -0.063328 |
6 | 7 | 0.121668 | 1.2076 | -0.00204021 | 1.6278 |
7 | 8 | 0.354493 | 1.03753 | -0.385684 | 0.519818 |
8 | 9 | 1.68658 | -1.32596 | 1.42898 | -2.08935 |
9 | 10 | -0.12982 | 0.631523 | -0.586538 | 0.29072 |
Average | 5.5 | 0.174294 | 0.128612 | 0.245891 | 0.284508 |
PrettyPandas(df).average(axis=1)
A | B | C | D | E | Average | |
---|---|---|---|---|---|---|
0 | 1 | 1.32921 | -0.770033 | -0.31628 | -0.99081 | 0.0504176 |
1 | 2 | -1.07082 | -1.43871 | 0.564417 | 0.295722 | 0.0701218 |
2 | 3 | -1.6264 | 0.219565 | 0.678805 | 1.88927 | 0.832248 |
3 | 4 | 0.961538 | 0.104011 | -0.481165 | 0.850229 | 1.08692 |
4 | 5 | 1.45342 | 1.05774 | 0.165562 | 0.515018 | 1.63835 |
5 | 6 | -1.33694 | 0.562861 | 1.39285 | -0.063328 | 1.31109 |
6 | 7 | 0.121668 | 1.2076 | -0.00204021 | 1.6278 | 1.99101 |
7 | 8 | 0.354493 | 1.03753 | -0.385684 | 0.519818 | 1.90523 |
8 | 9 | 1.68658 | -1.32596 | 1.42898 | -2.08935 | 1.74005 |
9 | 10 | -0.12982 | 0.631523 | -0.586538 | 0.29072 | 2.04118 |
PrettyPandas(df).average(axis=None)
A | B | C | D | E | Average | |
---|---|---|---|---|---|---|
0 | 1 | 1.32921 | -0.770033 | -0.31628 | -0.99081 | 0.0504176 |
1 | 2 | -1.07082 | -1.43871 | 0.564417 | 0.295722 | 0.0701218 |
2 | 3 | -1.6264 | 0.219565 | 0.678805 | 1.88927 | 0.832248 |
3 | 4 | 0.961538 | 0.104011 | -0.481165 | 0.850229 | 1.08692 |
4 | 5 | 1.45342 | 1.05774 | 0.165562 | 0.515018 | 1.63835 |
5 | 6 | -1.33694 | 0.562861 | 1.39285 | -0.063328 | 1.31109 |
6 | 7 | 0.121668 | 1.2076 | -0.00204021 | 1.6278 | 1.99101 |
7 | 8 | 0.354493 | 1.03753 | -0.385684 | 0.519818 | 1.90523 |
8 | 9 | 1.68658 | -1.32596 | 1.42898 | -2.08935 | 1.74005 |
9 | 10 | -0.12982 | 0.631523 | -0.586538 | 0.29072 | 2.04118 |
Average | 5.5 | 0.174294 | 0.128612 | 0.245891 | 0.284508 |
PrettyPandas(df).min().max()
A | B | C | D | E | |
---|---|---|---|---|---|
0 | 1 | 1.32921 | -0.770033 | -0.31628 | -0.99081 |
1 | 2 | -1.07082 | -1.43871 | 0.564417 | 0.295722 |
2 | 3 | -1.6264 | 0.219565 | 0.678805 | 1.88927 |
3 | 4 | 0.961538 | 0.104011 | -0.481165 | 0.850229 |
4 | 5 | 1.45342 | 1.05774 | 0.165562 | 0.515018 |
5 | 6 | -1.33694 | 0.562861 | 1.39285 | -0.063328 |
6 | 7 | 0.121668 | 1.2076 | -0.00204021 | 1.6278 |
7 | 8 | 0.354493 | 1.03753 | -0.385684 | 0.519818 |
8 | 9 | 1.68658 | -1.32596 | 1.42898 | -2.08935 |
9 | 10 | -0.12982 | 0.631523 | -0.586538 | 0.29072 |
Minimum | 1 | -1.6264 | -1.43871 | -0.586538 | -2.08935 |
Maximum | 10 | 1.68658 | 1.2076 | 1.42898 | 1.88927 |
PrettyPandas(df).summary(np.mean, "Average")
A | B | C | D | E | |
---|---|---|---|---|---|
0 | 1 | 1.32921 | -0.770033 | -0.31628 | -0.99081 |
1 | 2 | -1.07082 | -1.43871 | 0.564417 | 0.295722 |
2 | 3 | -1.6264 | 0.219565 | 0.678805 | 1.88927 |
3 | 4 | 0.961538 | 0.104011 | -0.481165 | 0.850229 |
4 | 5 | 1.45342 | 1.05774 | 0.165562 | 0.515018 |
5 | 6 | -1.33694 | 0.562861 | 1.39285 | -0.063328 |
6 | 7 | 0.121668 | 1.2076 | -0.00204021 | 1.6278 |
7 | 8 | 0.354493 | 1.03753 | -0.385684 | 0.519818 |
8 | 9 | 1.68658 | -1.32596 | 1.42898 | -2.08935 |
9 | 10 | -0.12982 | 0.631523 | -0.586538 | 0.29072 |
Average | 5.5 | 0.174294 | 0.128612 | 0.245891 | 0.284508 |
def count_greater_than_zero(column):
return (column > 0).sum()
PrettyPandas(df).summary(count_greater_than_zero, title="> 0")
A | B | C | D | E | |
---|---|---|---|---|---|
0 | 1 | 1.32921 | -0.770033 | -0.31628 | -0.99081 |
1 | 2 | -1.07082 | -1.43871 | 0.564417 | 0.295722 |
2 | 3 | -1.6264 | 0.219565 | 0.678805 | 1.88927 |
3 | 4 | 0.961538 | 0.104011 | -0.481165 | 0.850229 |
4 | 5 | 1.45342 | 1.05774 | 0.165562 | 0.515018 |
5 | 6 | -1.33694 | 0.562861 | 1.39285 | -0.063328 |
6 | 7 | 0.121668 | 1.2076 | -0.00204021 | 1.6278 |
7 | 8 | 0.354493 | 1.03753 | -0.385684 | 0.519818 |
8 | 9 | 1.68658 | -1.32596 | 1.42898 | -2.08935 |
9 | 10 | -0.12982 | 0.631523 | -0.586538 | 0.29072 |
> 0 | 10 | 6 | 7 | 5 | 7 |
PrettyPandas(df).multi_summary([np.mean, np.sum],
['Average', 'Total'],
axis=None)
A | B | C | D | E | Average | Total | |
---|---|---|---|---|---|---|---|
0 | 1 | 1.32921 | -0.770033 | -0.31628 | -0.99081 | 0.0504176 | 0.252088 |
1 | 2 | -1.07082 | -1.43871 | 0.564417 | 0.295722 | 0.0701218 | 0.350609 |
2 | 3 | -1.6264 | 0.219565 | 0.678805 | 1.88927 | 0.832248 | 4.16124 |
3 | 4 | 0.961538 | 0.104011 | -0.481165 | 0.850229 | 1.08692 | 5.43461 |
4 | 5 | 1.45342 | 1.05774 | 0.165562 | 0.515018 | 1.63835 | 8.19174 |
5 | 6 | -1.33694 | 0.562861 | 1.39285 | -0.063328 | 1.31109 | 6.55545 |
6 | 7 | 0.121668 | 1.2076 | -0.00204021 | 1.6278 | 1.99101 | 9.95503 |
7 | 8 | 0.354493 | 1.03753 | -0.385684 | 0.519818 | 1.90523 | 9.52615 |
8 | 9 | 1.68658 | -1.32596 | 1.42898 | -2.08935 | 1.74005 | 8.70025 |
9 | 10 | -0.12982 | 0.631523 | -0.586538 | 0.29072 | 2.04118 | 10.2059 |
Average | 5.5 | 0.174294 | 0.128612 | 0.245891 | 0.284508 | ||
Total | 55 | 1.74294 | 1.28612 | 2.45891 | 2.84508 |
from prettypandas import as_percent, as_currency, as_unit
df.style.format(as_percent(), subset='E')
A | B | C | D | E | |
---|---|---|---|---|---|
0 | 1 | 1.32921 | -0.770033 | -0.31628 | -99.08% |
1 | 2 | -1.07082 | -1.43871 | 0.564417 | 29.57% |
2 | 3 | -1.6264 | 0.219565 | 0.678805 | 188.93% |
3 | 4 | 0.961538 | 0.104011 | -0.481165 | 85.02% |
4 | 5 | 1.45342 | 1.05774 | 0.165562 | 51.50% |
5 | 6 | -1.33694 | 0.562861 | 1.39285 | -6.33% |
6 | 7 | 0.121668 | 1.2076 | -0.00204021 | 162.78% |
7 | 8 | 0.354493 | 1.03753 | -0.385684 | 51.98% |
8 | 9 | 1.68658 | -1.32596 | 1.42898 | -208.94% |
9 | 10 | -0.12982 | 0.631523 | -0.586538 | 29.07% |
PrettyPandas(df).as_percent(subset='A') # Format just column A
A | B | C | D | E | |
---|---|---|---|---|---|
0 | 100.00% | 1.32921 | -0.770033 | -0.31628 | -0.99081 |
1 | 200.00% | -1.07082 | -1.43871 | 0.564417 | 0.295722 |
2 | 300.00% | -1.6264 | 0.219565 | 0.678805 | 1.88927 |
3 | 400.00% | 0.961538 | 0.104011 | -0.481165 | 0.850229 |
4 | 500.00% | 1.45342 | 1.05774 | 0.165562 | 0.515018 |
5 | 600.00% | -1.33694 | 0.562861 | 1.39285 | -0.063328 |
6 | 700.00% | 0.121668 | 1.2076 | -0.00204021 | 1.6278 |
7 | 800.00% | 0.354493 | 1.03753 | -0.385684 | 0.519818 |
8 | 900.00% | 1.68658 | -1.32596 | 1.42898 | -2.08935 |
9 | 1000.00% | -0.12982 | 0.631523 | -0.586538 | 0.29072 |
PrettyPandas(df).as_percent(subset=['A', 'B']) # Format columns A and B
A | B | C | D | E | |
---|---|---|---|---|---|
0 | 100.00% | 132.92% | -0.770033 | -0.31628 | -0.99081 |
1 | 200.00% | -107.08% | -1.43871 | 0.564417 | 0.295722 |
2 | 300.00% | -162.64% | 0.219565 | 0.678805 | 1.88927 |
3 | 400.00% | 96.15% | 0.104011 | -0.481165 | 0.850229 |
4 | 500.00% | 145.34% | 1.05774 | 0.165562 | 0.515018 |
5 | 600.00% | -133.69% | 0.562861 | 1.39285 | -0.063328 |
6 | 700.00% | 12.17% | 1.2076 | -0.00204021 | 1.6278 |
7 | 800.00% | 35.45% | 1.03753 | -0.385684 | 0.519818 |
8 | 900.00% | 168.66% | -1.32596 | 1.42898 | -2.08935 |
9 | 1000.00% | -12.98% | 0.631523 | -0.586538 | 0.29072 |
PrettyPandas(df).as_currency('GBP')
A | B | C | D | E | |
---|---|---|---|---|---|
0 | £1.00 | £1.33 | -£0.77 | -£0.32 | -£0.99 |
1 | £2.00 | -£1.07 | -£1.44 | £0.56 | £0.30 |
2 | £3.00 | -£1.63 | £0.22 | £0.68 | £1.89 |
3 | £4.00 | £0.96 | £0.10 | -£0.48 | £0.85 |
4 | £5.00 | £1.45 | £1.06 | £0.17 | £0.52 |
5 | £6.00 | -£1.34 | £0.56 | £1.39 | -£0.06 |
6 | £7.00 | £0.12 | £1.21 | -£0.00 | £1.63 |
7 | £8.00 | £0.35 | £1.04 | -£0.39 | £0.52 |
8 | £9.00 | £1.69 | -£1.33 | £1.43 | -£2.09 |
9 | £10.00 | -£0.13 | £0.63 | -£0.59 | £0.29 |
PrettyPandas(df).as_percent(precision=3)
A | B | C | D | E | |
---|---|---|---|---|---|
0 | 100.000% | 132.921% | -77.003% | -31.628% | -99.081% |
1 | 200.000% | -107.082% | -143.871% | 56.442% | 29.572% |
2 | 300.000% | -162.640% | 21.957% | 67.880% | 188.927% |
3 | 400.000% | 96.154% | 10.401% | -48.117% | 85.023% |
4 | 500.000% | 145.342% | 105.774% | 16.556% | 51.502% |
5 | 600.000% | -133.694% | 56.286% | 139.285% | -6.333% |
6 | 700.000% | 12.167% | 120.760% | -0.204% | 162.780% |
7 | 800.000% | 35.449% | 103.753% | -38.568% | 51.982% |
8 | 900.000% | 168.658% | -132.596% | 142.898% | -208.935% |
9 | 1000.000% | -12.982% | 63.152% | -58.654% | 29.072% |
PrettyPandas(df).as_currency(currency="GBP")
A | B | C | D | E | |
---|---|---|---|---|---|
0 | £1.00 | £1.33 | -£0.77 | -£0.32 | -£0.99 |
1 | £2.00 | -£1.07 | -£1.44 | £0.56 | £0.30 |
2 | £3.00 | -£1.63 | £0.22 | £0.68 | £1.89 |
3 | £4.00 | £0.96 | £0.10 | -£0.48 | £0.85 |
4 | £5.00 | £1.45 | £1.06 | £0.17 | £0.52 |
5 | £6.00 | -£1.34 | £0.56 | £1.39 | -£0.06 |
6 | £7.00 | £0.12 | £1.21 | -£0.00 | £1.63 |
7 | £8.00 | £0.35 | £1.04 | -£0.39 | £0.52 |
8 | £9.00 | £1.69 | -£1.33 | £1.43 | -£2.09 |
9 | £10.00 | -£0.13 | £0.63 | -£0.59 | £0.29 |
PrettyPandas(df).as_unit(' mol', location='suffix')
A | B | C | D | E | |
---|---|---|---|---|---|
0 | 1.00 mol | 1.33 mol | -0.77 mol | -0.32 mol | -0.99 mol |
1 | 2.00 mol | -1.07 mol | -1.44 mol | 0.56 mol | 0.30 mol |
2 | 3.00 mol | -1.63 mol | 0.22 mol | 0.68 mol | 1.89 mol |
3 | 4.00 mol | 0.96 mol | 0.10 mol | -0.48 mol | 0.85 mol |
4 | 5.00 mol | 1.45 mol | 1.06 mol | 0.17 mol | 0.52 mol |
5 | 6.00 mol | -1.34 mol | 0.56 mol | 1.39 mol | -0.06 mol |
6 | 7.00 mol | 0.12 mol | 1.21 mol | -0.00 mol | 1.63 mol |
7 | 8.00 mol | 0.35 mol | 1.04 mol | -0.39 mol | 0.52 mol |
8 | 9.00 mol | 1.69 mol | -1.33 mol | 1.43 mol | -2.09 mol |
9 | 10.00 mol | -0.13 mol | 0.63 mol | -0.59 mol | 0.29 mol |
PrettyPandas(df).as_percent(subset=['B'])
A | B | C | D | E | |
---|---|---|---|---|---|
0 | 1 | 132.92% | -0.770033 | -0.31628 | -0.99081 |
1 | 2 | -107.08% | -1.43871 | 0.564417 | 0.295722 |
2 | 3 | -162.64% | 0.219565 | 0.678805 | 1.88927 |
3 | 4 | 96.15% | 0.104011 | -0.481165 | 0.850229 |
4 | 5 | 145.34% | 1.05774 | 0.165562 | 0.515018 |
5 | 6 | -133.69% | 0.562861 | 1.39285 | -0.063328 |
6 | 7 | 12.17% | 1.2076 | -0.00204021 | 1.6278 |
7 | 8 | 35.45% | 1.03753 | -0.385684 | 0.519818 |
8 | 9 | 168.66% | -1.32596 | 1.42898 | -2.08935 |
9 | 10 | -12.98% | 0.631523 | -0.586538 | 0.29072 |
PrettyPandas(df).as_percent(subset=['B']).total()
A | B | C | D | E | |
---|---|---|---|---|---|
0 | 1 | 132.92% | -0.770033 | -0.31628 | -0.99081 |
1 | 2 | -107.08% | -1.43871 | 0.564417 | 0.295722 |
2 | 3 | -162.64% | 0.219565 | 0.678805 | 1.88927 |
3 | 4 | 96.15% | 0.104011 | -0.481165 | 0.850229 |
4 | 5 | 145.34% | 1.05774 | 0.165562 | 0.515018 |
5 | 6 | -133.69% | 0.562861 | 1.39285 | -0.063328 |
6 | 7 | 12.17% | 1.2076 | -0.00204021 | 1.6278 |
7 | 8 | 35.45% | 1.03753 | -0.385684 | 0.519818 |
8 | 9 | 168.66% | -1.32596 | 1.42898 | -2.08935 |
9 | 10 | -12.98% | 0.631523 | -0.586538 | 0.29072 |
Total | 55 | 174.29% | 1.28612 | 2.45891 | 2.84508 |
(
df.pipe(PrettyPandas)
.as_percent(precision=0)
.median()
.style
.background_gradient()
)
A | B | C | D | E | |
---|---|---|---|---|---|
0 | 100% | 133% | -77% | -32% | -99% |
1 | 200% | -107% | -144% | 56% | 30% |
2 | 300% | -163% | 22% | 68% | 189% |
3 | 400% | 96% | 10% | -48% | 85% |
4 | 500% | 145% | 106% | 17% | 52% |
5 | 600% | -134% | 56% | 139% | -6% |
6 | 700% | 12% | 121% | -0% | 163% |
7 | 800% | 35% | 104% | -39% | 52% |
8 | 900% | 169% | -133% | 143% | -209% |
9 | 1000% | -13% | 63% | -59% | 29% |
Median | 550% | 24% | 39% | 8% | 41% |
PrettyPandas(df).as_percent(subset=pd.IndexSlice[3,:], precision=2)
A | B | C | D | E | |
---|---|---|---|---|---|
0 | 1 | 1.32921 | -0.770033 | -0.31628 | -0.99081 |
1 | 2 | -1.07082 | -1.43871 | 0.564417 | 0.295722 |
2 | 3 | -1.6264 | 0.219565 | 0.678805 | 1.88927 |
3 | 400.00% | 96.15% | 10.40% | -48.12% | 85.02% |
4 | 5 | 1.45342 | 1.05774 | 0.165562 | 0.515018 |
5 | 6 | -1.33694 | 0.562861 | 1.39285 | -0.063328 |
6 | 7 | 0.121668 | 1.2076 | -0.00204021 | 1.6278 |
7 | 8 | 0.354493 | 1.03753 | -0.385684 | 0.519818 |
8 | 9 | 1.68658 | -1.32596 | 1.42898 | -2.08935 |
9 | 10 | -0.12982 | 0.631523 | -0.586538 | 0.29072 |
first_row_idx = pd.IndexSlice[0, :]
second_row_idx = pd.IndexSlice[1, :]
(
df.pipe(PrettyPandas)
.as_currency(subset=first_row_idx)
.as_percent(subset=second_row_idx)
.total(axis=1)
)
A | B | C | D | E | Total | |
---|---|---|---|---|---|---|
0 | $1.00 | $1.33 | -$0.77 | -$0.32 | -$0.99 | $0.25 |
1 | 200.00% | -107.08% | -143.87% | 56.44% | 29.57% | 35.06% |
2 | 3 | -1.6264 | 0.219565 | 0.678805 | 1.88927 | 4.16124 |
3 | 4 | 0.961538 | 0.104011 | -0.481165 | 0.850229 | 5.43461 |
4 | 5 | 1.45342 | 1.05774 | 0.165562 | 0.515018 | 8.19174 |
5 | 6 | -1.33694 | 0.562861 | 1.39285 | -0.063328 | 6.55545 |
6 | 7 | 0.121668 | 1.2076 | -0.00204021 | 1.6278 | 9.95503 |
7 | 8 | 0.354493 | 1.03753 | -0.385684 | 0.519818 | 9.52615 |
8 | 9 | 1.68658 | -1.32596 | 1.42898 | -2.08935 | 8.70025 |
9 | 10 | -0.12982 | 0.631523 | -0.586538 | 0.29072 | 10.2059 |
(
df
.pipe(PrettyPandas)
.as_currency('GBP', subset='A')
.as_percent(subset='B')
.total()
.average()
)
A | B | C | D | E | |
---|---|---|---|---|---|
0 | £1.00 | 132.92% | -0.770033 | -0.31628 | -0.99081 |
1 | £2.00 | -107.08% | -1.43871 | 0.564417 | 0.295722 |
2 | £3.00 | -162.64% | 0.219565 | 0.678805 | 1.88927 |
3 | £4.00 | 96.15% | 0.104011 | -0.481165 | 0.850229 |
4 | £5.00 | 145.34% | 1.05774 | 0.165562 | 0.515018 |
5 | £6.00 | -133.69% | 0.562861 | 1.39285 | -0.063328 |
6 | £7.00 | 12.17% | 1.2076 | -0.00204021 | 1.6278 |
7 | £8.00 | 35.45% | 1.03753 | -0.385684 | 0.519818 |
8 | £9.00 | 168.66% | -1.32596 | 1.42898 | -2.08935 |
9 | £10.00 | -12.98% | 0.631523 | -0.586538 | 0.29072 |
Total | £55.00 | 174.29% | 1.28612 | 2.45891 | 2.84508 |
Average | £5.50 | 17.43% | 0.128612 | 0.245891 | 0.284508 |
(
df
.pipe(PrettyPandas)
.total(axis=1)
.to_frame()
)
A | B | C | D | E | Total | |
---|---|---|---|---|---|---|
0 | 1.0 | 1.329212 | -0.770033 | -0.316280 | -0.990810 | 0.252088 |
1 | 2.0 | -1.070816 | -1.438713 | 0.564417 | 0.295722 | 0.350609 |
2 | 3.0 | -1.626404 | 0.219565 | 0.678805 | 1.889273 | 4.161238 |
3 | 4.0 | 0.961538 | 0.104011 | -0.481165 | 0.850229 | 5.434613 |
4 | 5.0 | 1.453425 | 1.057737 | 0.165562 | 0.515018 | 8.191742 |
5 | 6.0 | -1.336936 | 0.562861 | 1.392855 | -0.063328 | 6.555452 |
6 | 7.0 | 0.121668 | 1.207603 | -0.002040 | 1.627796 | 9.955026 |
7 | 8.0 | 0.354493 | 1.037528 | -0.385684 | 0.519818 | 9.526155 |
8 | 9.0 | 1.686583 | -1.325963 | 1.428984 | -2.089354 | 8.700249 |
9 | 10.0 | -0.129820 | 0.631523 | -0.586538 | 0.290720 | 10.205885 |