Pandas para usuarios de Microsoft Excel — 19:50 min

19:50 min | Última modificación: Octubre 6, 2021

Preparación

[1]:

import numpy as np
import pandas as pd

Lectura de un archivo CSV

[2]:

url = 'https://raw.github.com/pandas-dev/pandas/master/pandas/tests/io/data/csv/tips.csv'
tips = pd.read_csv(url)
tips.head(n=5)

[2]:

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4

Autofiltro en Microsoft Excel

## Ordenamiento de una columna

[3]:

tips.sort_values(
    by=["sex", "tip"],
    ascending=[True, False],
).head(n=10)

[3]:

	total_bill	tip	sex	smoker	day	time	size
214	28.17	6.50	Female	Yes	Sat	Dinner	3
52	34.81	5.20	Female	No	Sun	Dinner	4
85	34.83	5.17	Female	No	Thur	Lunch	4
155	29.85	5.14	Female	No	Sun	Dinner	5
11	35.26	5.00	Female	No	Sun	Dinner	4
73	25.28	5.00	Female	Yes	Sat	Dinner	2
143	27.05	5.00	Female	No	Thur	Lunch	6
197	43.11	5.00	Female	Yes	Thur	Lunch	4
238	35.83	4.67	Female	No	Sat	Dinner	3
93	16.32	4.30	Female	Yes	Fri	Dinner	2

Filtrado

[4]:

#
# Filtro "Mayor que"
#
tips[tips['tip'] > 6.50].head(n=10)

[4]:

	total_bill	tip	sex	smoker	day	time	size
23	39.42	7.58	Male	No	Sat	Dinner	4
59	48.27	6.73	Male	No	Sat	Dinner	4
141	34.30	6.70	Male	No	Thur	Lunch	6
170	50.81	10.00	Male	Yes	Sat	Dinner	3
212	48.33	9.00	Male	No	Sat	Dinner	4

[5]:

#
# Comando equivalente con query
#
tips.query('tip > 6.50').head(n=10)

[5]:

	total_bill	tip	sex	smoker	day	time	size
23	39.42	7.58	Male	No	Sat	Dinner	4
59	48.27	6.73	Male	No	Sat	Dinner	4
141	34.30	6.70	Male	No	Thur	Lunch	6
170	50.81	10.00	Male	Yes	Sat	Dinner	3
212	48.33	9.00	Male	No	Sat	Dinner	4

[6]:

#
# Filtro con condicional compuesto
#
tips[(tips['tip'] > 6.50) & (tips['time'] == 'Dinner')].head(n=10)

[6]:

	total_bill	tip	sex	smoker	day	time	size
23	39.42	7.58	Male	No	Sat	Dinner	4
59	48.27	6.73	Male	No	Sat	Dinner	4
170	50.81	10.00	Male	Yes	Sat	Dinner	3
212	48.33	9.00	Male	No	Sat	Dinner	4

[7]:

#
# Filtro "Por encima del promedio"
#
tips[
    tips['tip'] > tips['tip'].mean()
].tail(n=10)

[7]:

	total_bill	tip	sex	smoker	day	time	size
219	30.14	3.09	Female	Yes	Sat	Dinner	4
221	13.42	3.48	Female	Yes	Fri	Lunch	2
223	15.98	3.00	Female	No	Fri	Lunch	3
227	20.45	3.00	Male	No	Sat	Dinner	4
231	15.69	3.00	Male	Yes	Sat	Dinner	3
232	11.61	3.39	Male	No	Sat	Dinner	2
234	15.53	3.00	Male	Yes	Sat	Dinner	2
238	35.83	4.67	Female	No	Sat	Dinner	3
239	29.03	5.92	Male	No	Sat	Dinner	3
243	18.78	3.00	Female	No	Thur	Dinner	2

[8]:

#
# Cómputo del promedio
#
tips['tip'].mean()

[8]:

2.99827868852459

[9]:

#
# Diez mejores
#
tips.nlargest(10, 'tip')

[9]:

	total_bill	tip	sex	smoker	day	time	size
170	50.81	10.00	Male	Yes	Sat	Dinner	3
212	48.33	9.00	Male	No	Sat	Dinner	4
23	39.42	7.58	Male	No	Sat	Dinner	4
59	48.27	6.73	Male	No	Sat	Dinner	4
141	34.30	6.70	Male	No	Thur	Lunch	6
183	23.17	6.50	Male	Yes	Sun	Dinner	4
214	28.17	6.50	Female	Yes	Sat	Dinner	3
47	32.40	6.00	Male	No	Sun	Dinner	4
239	29.03	5.92	Male	No	Sat	Dinner	3
88	24.71	5.85	Male	No	Thur	Lunch	2

[10]:

#
# Diez inferiores
#
tips.nsmallest(10, 'tip')

[10]:

	total_bill	tip	sex	smoker	day	time	size
67	3.07	1.00	Female	Yes	Sat	Dinner	1
92	5.75	1.00	Female	Yes	Fri	Dinner	2
111	7.25	1.00	Female	No	Sat	Dinner	1
236	12.60	1.00	Male	Yes	Sat	Dinner	2
0	16.99	1.01	Female	No	Sun	Dinner	2
215	12.90	1.10	Female	Yes	Sat	Dinner	2
237	32.83	1.17	Male	Yes	Sat	Dinner	2
75	10.51	1.25	Male	No	Sat	Dinner	2
135	8.51	1.25	Female	No	Thur	Lunch	2
235	10.07	1.25	Male	No	Sat	Dinner	2

Eliminación de una columna

[11]:

tips.drop("day", axis=1)

[11]:

	total_bill	tip	sex	smoker	time	size
0	16.99	1.01	Female	No	Dinner	2
1	10.34	1.66	Male	No	Dinner	3
2	21.01	3.50	Male	No	Dinner	3
3	23.68	3.31	Male	No	Dinner	2
4	24.59	3.61	Female	No	Dinner	4
...	...	...	...	...	...	...
239	29.03	5.92	Male	No	Dinner	3
240	27.18	2.00	Female	Yes	Dinner	2
241	22.67	2.00	Male	Yes	Dinner	2
242	17.82	1.75	Male	No	Dinner	2
243	18.78	3.00	Female	No	Dinner	2

244 rows × 6 columns

Cambio del nombre de una columna

[12]:

tips.rename(
    columns={"smoker": "is_smoker"},
)

[12]:

	total_bill	tip	sex	is_smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4
...	...	...	...	...	...	...	...
239	29.03	5.92	Male	No	Sat	Dinner	3
240	27.18	2.00	Female	Yes	Sat	Dinner	2
241	22.67	2.00	Male	Yes	Sat	Dinner	2
242	17.82	1.75	Male	No	Sat	Dinner	2
243	18.78	3.00	Female	No	Thur	Dinner	2

244 rows × 7 columns

Subtotales

[13]:

#
# Contar -- size()
#
tips.groupby('sex').size()

[13]:

sex
Female     87
Male      157
dtype: int64

[14]:

#
# Contar -- size() vs count()
#
tips.groupby('sex').count()

[14]:

	total_bill	tip	smoker	day	time	size
sex
Female	87	87	87	87	87	87
Male	157	157	157	157	157	157

[15]:

#
# Contar -- max(), min(), ...
#
tips.groupby('sex').max()

[15]:

	total_bill	tip	smoker	day	time	size
sex
Female	44.30	6.5	Yes	Thur	Lunch	6
Male	50.81	10.0	Yes	Thur	Lunch	6

[16]:

#
# Funciones diferenciales por columna
#
tips.groupby("sex").agg(
    {
        "tip": np.max,
        "total_bill": np.mean,
    }
)

[16]:

	tip	total_bill
sex
Female	6.5	18.056897
Male	10.0	20.744076

[17]:

tips.groupby(
    "sex",
    as_index=True,
).agg(
    {
        "tip": np.max,
        "total_bill": np.mean,
    }
)

[17]:

	tip	total_bill
sex
Female	6.5	18.056897
Male	10.0	20.744076

[18]:

#
# Opción as_index
#
tips.groupby(
    "sex",
    as_index=True,
).agg({"tip": np.max, "total_bill": np.mean})

[18]:

	tip	total_bill
sex
Female	6.5	18.056897
Male	10.0	20.744076

[19]:

tips.groupby(
    "sex",
    as_index=False,
).agg({"tip": np.max, "total_bill": np.mean})

[19]:

	sex	tip	total_bill
0	Female	6.5	18.056897
1	Male	10.0	20.744076

Computos sobre las columnas de una tabla

[20]:

tips.columns

[20]:

Index(['total_bill', 'tip', 'sex', 'smoker', 'day', 'time', 'size'], dtype='object')

[21]:

#
# Una nueva columna
#
tips.assign(tax10=0.1 * tips.total_bill)

[21]:

	total_bill	tip	sex	smoker	day	time	size	tax10
0	16.99	1.01	Female	No	Sun	Dinner	2	1.699
1	10.34	1.66	Male	No	Sun	Dinner	3	1.034
2	21.01	3.50	Male	No	Sun	Dinner	3	2.101
3	23.68	3.31	Male	No	Sun	Dinner	2	2.368
4	24.59	3.61	Female	No	Sun	Dinner	4	2.459
...	...	...	...	...	...	...	...	...
239	29.03	5.92	Male	No	Sat	Dinner	3	2.903
240	27.18	2.00	Female	Yes	Sat	Dinner	2	2.718
241	22.67	2.00	Male	Yes	Sat	Dinner	2	2.267
242	17.82	1.75	Male	No	Sat	Dinner	2	1.782
243	18.78	3.00	Female	No	Thur	Dinner	2	1.878

244 rows × 8 columns

[22]:

#
# Computos con valores respecto a otras filas
#
x = pd.DataFrame([10, 21, 32, 42, 55, 61, 74], columns=['Valores'])
x

[22]:

	Valores
0	10
1	21
2	32
3	42
4	55
5	61
6	74

[23]:

x.shift(periods=2, fill_value=0)

[23]:

	Valores
0	0
1	0
2	10
3	21
4	32
5	42
6	55

[24]:

x.shift(periods=-2, fill_value=0)

[24]:

	Valores
0	32
1	42
2	55
3	61
4	74
5	0
6	0

Escenarios

[25]:

#
# Alternativa 1
#
escenarios = [
    (1, 2, 3),
    (4, 5, 6),
    (7, 8, 9),
    (10, 11, 12),
]
a, b, c = escenarios[1]
a + b + c

[25]:

[26]:

#
# Alterantiva 2
#
def f(a, b, c):
    return a + b + c


escenarios = [
    {"a": 1, "b": 2, "c": 3},
    {"a": 4, "b": 5, "c": 6},
    {"a": 7, "b": 8, "c": 9},
    {"a": 10, "b": 11, "c": 12},
]
f(**escenarios[1])

[26]:

Tablas dinámicas

[27]:

#
# Del ejemplo anterior
#
def f(a, b, c):
    return a + b + c

escenarios = [
    {"a": 1, "b": 2, "c": 3},
    {"a": 4, "b": 5, "c": 6},
    {"a": 7, "b": 8, "c": 9},
    {"a": 10, "b": 11, "c": 12},
]

z = pd.DataFrame(range(len(escenarios)), columns=['Escenario'])
z

[27]:

	Escenario
0	0
1	1
2	2
3	3

[28]:

z['f'] = [f(**escenarios[i]) for i in z['Escenario']]
z

[28]:

	Escenario	f
0	0	6
1	1	15
2	2	24
3	3	33

Solver

[29]:

def f0(x):
    return sum([i**2 for i in x])

# 1^2 + 2^2 + 3^2 = 14
f0([1, 2, 3])

[29]:

[30]:

from scipy.optimize import minimize

minimize(f0, x0=[1, 2, 3]).x

[30]:

array([-1.17452324e-08, -8.75678727e-09, -3.66353142e-09])

Styles

Basado en:

https://pandas.pydata.org/pandas-docs/stable/user_guide/style.html

[31]:

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.iloc[3, 3] = np.nan
df.iloc[0, 2] = np.nan

df

[31]:

	A	B	C	D	E
0	1.0	1.329212	NaN	-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	NaN	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

[32]:

def color_negative_red(val):
    color = 'red' if val < 0 else 'black'
    return 'color: %s' % color

df.style.applymap(color_negative_red)

[32]:

	A	B	C	D	E
0	1.000000	1.329212	nan	-0.316280	-0.990810
1	2.000000	-1.070816	-1.438713	0.564417	0.295722
2	3.000000	-1.626404	0.219565	0.678805	1.889273
3	4.000000	0.961538	0.104011	nan	0.850229
4	5.000000	1.453425	1.057737	0.165562	0.515018
5	6.000000	-1.336936	0.562861	1.392855	-0.063328
6	7.000000	0.121668	1.207603	-0.002040	1.627796
7	8.000000	0.354493	1.037528	-0.385684	0.519818
8	9.000000	1.686583	-1.325963	1.428984	-2.089354
9	10.000000	-0.129820	0.631523	-0.586538	0.290720

[33]:

def highlight_max(s):
    is_max = s == s.max()
    return ['background-color: yellow' if v else '' for v in is_max]

df.style.apply(highlight_max)

[33]:

	A	B	C	D	E
0	1.000000	1.329212	nan	-0.316280	-0.990810
1	2.000000	-1.070816	-1.438713	0.564417	0.295722
2	3.000000	-1.626404	0.219565	0.678805	1.889273
3	4.000000	0.961538	0.104011	nan	0.850229
4	5.000000	1.453425	1.057737	0.165562	0.515018
5	6.000000	-1.336936	0.562861	1.392855	-0.063328
6	7.000000	0.121668	1.207603	-0.002040	1.627796
7	8.000000	0.354493	1.037528	-0.385684	0.519818
8	9.000000	1.686583	-1.325963	1.428984	-2.089354
9	10.000000	-0.129820	0.631523	-0.586538	0.290720

[34]:

df.style.\
    applymap(color_negative_red).\
    apply(highlight_max)

[34]:

	A	B	C	D	E
0	1.000000	1.329212	nan	-0.316280	-0.990810
1	2.000000	-1.070816	-1.438713	0.564417	0.295722
2	3.000000	-1.626404	0.219565	0.678805	1.889273
3	4.000000	0.961538	0.104011	nan	0.850229
4	5.000000	1.453425	1.057737	0.165562	0.515018
5	6.000000	-1.336936	0.562861	1.392855	-0.063328
6	7.000000	0.121668	1.207603	-0.002040	1.627796
7	8.000000	0.354493	1.037528	-0.385684	0.519818
8	9.000000	1.686583	-1.325963	1.428984	-2.089354
9	10.000000	-0.129820	0.631523	-0.586538	0.290720

[35]:

df.style.apply(highlight_max, subset=['B', 'C', 'D'])

[35]:

	A	B	C	D	E
0	1.000000	1.329212	nan	-0.316280	-0.990810
1	2.000000	-1.070816	-1.438713	0.564417	0.295722
2	3.000000	-1.626404	0.219565	0.678805	1.889273
3	4.000000	0.961538	0.104011	nan	0.850229
4	5.000000	1.453425	1.057737	0.165562	0.515018
5	6.000000	-1.336936	0.562861	1.392855	-0.063328
6	7.000000	0.121668	1.207603	-0.002040	1.627796
7	8.000000	0.354493	1.037528	-0.385684	0.519818
8	9.000000	1.686583	-1.325963	1.428984	-2.089354
9	10.000000	-0.129820	0.631523	-0.586538	0.290720

[36]:

df.style.applymap(color_negative_red, subset=pd.IndexSlice[2:5, ["B", "D"]])

[36]:

	A	B	C	D	E
0	1.000000	1.329212	nan	-0.316280	-0.990810
1	2.000000	-1.070816	-1.438713	0.564417	0.295722
2	3.000000	-1.626404	0.219565	0.678805	1.889273
3	4.000000	0.961538	0.104011	nan	0.850229
4	5.000000	1.453425	1.057737	0.165562	0.515018
5	6.000000	-1.336936	0.562861	1.392855	-0.063328
6	7.000000	0.121668	1.207603	-0.002040	1.627796
7	8.000000	0.354493	1.037528	-0.385684	0.519818
8	9.000000	1.686583	-1.325963	1.428984	-2.089354
9	10.000000	-0.129820	0.631523	-0.586538	0.290720

[37]:

df.style.format("{:.2%}")

[37]:

	A	B	C	D	E
0	100.00%	132.92%	nan%	-31.63%	-99.08%
1	200.00%	-107.08%	-143.87%	56.44%	29.57%
2	300.00%	-162.64%	21.96%	67.88%	188.93%
3	400.00%	96.15%	10.40%	nan%	85.02%
4	500.00%	145.34%	105.77%	16.56%	51.50%
5	600.00%	-133.69%	56.29%	139.29%	-6.33%
6	700.00%	12.17%	120.76%	-0.20%	162.78%
7	800.00%	35.45%	103.75%	-38.57%	51.98%
8	900.00%	168.66%	-132.60%	142.90%	-208.94%
9	1000.00%	-12.98%	63.15%	-58.65%	29.07%

[38]:

df.style.format({'B': "{:0<4.0f}", 'D': '{:+.2f}'})

[38]:

	A	B	C	D	E
0	1.000000	1000	nan	-0.32	-0.990810
1	2.000000	-100	-1.438713	+0.56	0.295722
2	3.000000	-200	0.219565	+0.68	1.889273
3	4.000000	1000	0.104011	+nan	0.850229
4	5.000000	1000	1.057737	+0.17	0.515018
5	6.000000	-100	0.562861	+1.39	-0.063328
6	7.000000	0000	1.207603	-0.00	1.627796
7	8.000000	0000	1.037528	-0.39	0.519818
8	9.000000	2000	-1.325963	+1.43	-2.089354
9	10.000000	-000	0.631523	-0.59	0.290720

[39]:

df.style.format({"B": lambda x: "±{:.2f}".format(abs(x))})

[39]:

	A	B	C	D	E
0	1.000000	±1.33	nan	-0.316280	-0.990810
1	2.000000	±1.07	-1.438713	0.564417	0.295722
2	3.000000	±1.63	0.219565	0.678805	1.889273
3	4.000000	±0.96	0.104011	nan	0.850229
4	5.000000	±1.45	1.057737	0.165562	0.515018
5	6.000000	±1.34	0.562861	1.392855	-0.063328
6	7.000000	±0.12	1.207603	-0.002040	1.627796
7	8.000000	±0.35	1.037528	-0.385684	0.519818
8	9.000000	±1.69	-1.325963	1.428984	-2.089354
9	10.000000	±0.13	0.631523	-0.586538	0.290720

[40]:

df.style.format("{:.2%}", na_rep="-")

[40]:

	A	B	C	D	E
0	100.00%	132.92%	-	-31.63%	-99.08%
1	200.00%	-107.08%	-143.87%	56.44%	29.57%
2	300.00%	-162.64%	21.96%	67.88%	188.93%
3	400.00%	96.15%	10.40%	-	85.02%
4	500.00%	145.34%	105.77%	16.56%	51.50%
5	600.00%	-133.69%	56.29%	139.29%	-6.33%
6	700.00%	12.17%	120.76%	-0.20%	162.78%
7	800.00%	35.45%	103.75%	-38.57%	51.98%
8	900.00%	168.66%	-132.60%	142.90%	-208.94%
9	1000.00%	-12.98%	63.15%	-58.65%	29.07%

[41]:

df.style.highlight_null(null_color='red')

[41]:

	A	B	C	D	E
0	1.000000	1.329212	nan	-0.316280	-0.990810
1	2.000000	-1.070816	-1.438713	0.564417	0.295722
2	3.000000	-1.626404	0.219565	0.678805	1.889273
3	4.000000	0.961538	0.104011	nan	0.850229
4	5.000000	1.453425	1.057737	0.165562	0.515018
5	6.000000	-1.336936	0.562861	1.392855	-0.063328
6	7.000000	0.121668	1.207603	-0.002040	1.627796
7	8.000000	0.354493	1.037528	-0.385684	0.519818
8	9.000000	1.686583	-1.325963	1.428984	-2.089354
9	10.000000	-0.129820	0.631523	-0.586538	0.290720

[42]:

import seaborn as sns

cm = sns.light_palette("green", as_cmap=True)

df.style.background_gradient(cmap=cm)

[42]:

	A	B	C	D	E
0	1.000000	1.329212	nan	-0.316280	-0.990810
1	2.000000	-1.070816	-1.438713	0.564417	0.295722
2	3.000000	-1.626404	0.219565	0.678805	1.889273
3	4.000000	0.961538	0.104011	nan	0.850229
4	5.000000	1.453425	1.057737	0.165562	0.515018
5	6.000000	-1.336936	0.562861	1.392855	-0.063328
6	7.000000	0.121668	1.207603	-0.002040	1.627796
7	8.000000	0.354493	1.037528	-0.385684	0.519818
8	9.000000	1.686583	-1.325963	1.428984	-2.089354
9	10.000000	-0.129820	0.631523	-0.586538	0.290720

[43]:

df.loc[:4].style.background_gradient(cmap='viridis')

[43]:

	A	B	C	D	E
0	1.000000	1.329212	nan	-0.316280	-0.990810
1	2.000000	-1.070816	-1.438713	0.564417	0.295722
2	3.000000	-1.626404	0.219565	0.678805	1.889273
3	4.000000	0.961538	0.104011	nan	0.850229
4	5.000000	1.453425	1.057737	0.165562	0.515018

[44]:

df.style.highlight_max(axis=0)

[44]:

	A	B	C	D	E
0	1.000000	1.329212	nan	-0.316280	-0.990810
1	2.000000	-1.070816	-1.438713	0.564417	0.295722
2	3.000000	-1.626404	0.219565	0.678805	1.889273
3	4.000000	0.961538	0.104011	nan	0.850229
4	5.000000	1.453425	1.057737	0.165562	0.515018
5	6.000000	-1.336936	0.562861	1.392855	-0.063328
6	7.000000	0.121668	1.207603	-0.002040	1.627796
7	8.000000	0.354493	1.037528	-0.385684	0.519818
8	9.000000	1.686583	-1.325963	1.428984	-2.089354
9	10.000000	-0.129820	0.631523	-0.586538	0.290720

[45]:

df.style.bar(subset=['A', 'B'], color='#d65f5f')

[45]:

	A	B	C	D	E
0	1.000000	1.329212	nan	-0.316280	-0.990810
1	2.000000	-1.070816	-1.438713	0.564417	0.295722
2	3.000000	-1.626404	0.219565	0.678805	1.889273
3	4.000000	0.961538	0.104011	nan	0.850229
4	5.000000	1.453425	1.057737	0.165562	0.515018
5	6.000000	-1.336936	0.562861	1.392855	-0.063328
6	7.000000	0.121668	1.207603	-0.002040	1.627796
7	8.000000	0.354493	1.037528	-0.385684	0.519818
8	9.000000	1.686583	-1.325963	1.428984	-2.089354
9	10.000000	-0.129820	0.631523	-0.586538	0.290720

[46]:

df.style.bar(subset=['A', 'B'], align='mid', color=['#d65f5f', '#5fba7d'])

[46]:

	A	B	C	D	E
0	1.000000	1.329212	nan	-0.316280	-0.990810
1	2.000000	-1.070816	-1.438713	0.564417	0.295722
2	3.000000	-1.626404	0.219565	0.678805	1.889273
3	4.000000	0.961538	0.104011	nan	0.850229
4	5.000000	1.453425	1.057737	0.165562	0.515018
5	6.000000	-1.336936	0.562861	1.392855	-0.063328
6	7.000000	0.121668	1.207603	-0.002040	1.627796
7	8.000000	0.354493	1.037528	-0.385684	0.519818
8	9.000000	1.686583	-1.325963	1.428984	-2.089354
9	10.000000	-0.129820	0.631523	-0.586538	0.290720

[47]:

df.style.hide_index()

[47]:

A	B	C	D	E
1.000000	1.329212	nan	-0.316280	-0.990810
2.000000	-1.070816	-1.438713	0.564417	0.295722
3.000000	-1.626404	0.219565	0.678805	1.889273
4.000000	0.961538	0.104011	nan	0.850229
5.000000	1.453425	1.057737	0.165562	0.515018
6.000000	-1.336936	0.562861	1.392855	-0.063328
7.000000	0.121668	1.207603	-0.002040	1.627796
8.000000	0.354493	1.037528	-0.385684	0.519818
9.000000	1.686583	-1.325963	1.428984	-2.089354
10.000000	-0.129820	0.631523	-0.586538	0.290720

[48]:

df.style.hide_columns(['C','D'])

[48]:

	A	B	E
0	1.000000	1.329212	-0.990810
1	2.000000	-1.070816	0.295722
2	3.000000	-1.626404	1.889273
3	4.000000	0.961538	0.850229
4	5.000000	1.453425	0.515018
5	6.000000	-1.336936	-0.063328
6	7.000000	0.121668	1.627796
7	8.000000	0.354493	0.519818
8	9.000000	1.686583	-2.089354
9	10.000000	-0.129820	0.290720