Pandas para usuarios de Microsoft Excel — 22:42 min#

  • Ultima modificación: Feb 01, 2024 | YouTube

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#

autofilter

## 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#

filtrado-excel

[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]:

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

[18]:
sex tip total_bill
0 Female 6.5 18.056897
1 Male 10.0 20.744076

Computos sobre las columnas de una tabla#

[19]:

tips.columns

[19]:

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

[20]:

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

[20]:
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

[21]:

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

[21]:
Valores
0 10
1 21
2 32
3 42
4 55
5 61
6 74 
[22]:

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

[22]:
Valores
0 0
1 0
2 10
3 21
4 32
5 42
6 55 
[23]:

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

[23]:
Valores
0 32
1 42
2 55
3 61
4 74
5 0
6 0

Escenarios#

[24]:

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

[24]:

15

[25]:

#
# 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])

[25]:

15

Tablas dinámicas#

[26]:

#
# 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

[26]:
Escenario
0 0
1 1
2 2
3 3 
[27]:

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

[27]:
Escenario f
0 0 6
1 1 15
2 2 24
3 3 33

Solver#

[28]:

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


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

[28]:

14

[29]:

from scipy.optimize import minimize

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

[29]:

array([-1.27600517e-08, -8.68161548e-09, -3.55077334e-09])

Styles#

Basado en:

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

[30]:

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

[30]:
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 
[31]:

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


df.style.applymap(color_negative_red)

[31]:
  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 
[32]:

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)

[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]:

df.style.applymap(color_negative_red).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.apply(highlight_max, subset=["B", "C", "D"])

[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.applymap(
    color_negative_red,
    subset=pd.IndexSlice[2:5, ["B", "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.format("{:.2%}")

[36]:
  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% 
[37]:

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

[37]:
  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 
[38]:

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

[38]:
  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 
[39]:

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

[39]:
  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% 
[40]:

df.style.highlight_null(color="red")

[40]:
  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 
[41]:

import seaborn as sns

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

df.style.background_gradient(cmap=cm)

[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]:

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

[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 
[43]:

df.style.highlight_max(axis=0)

[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
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 
[44]:

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

[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"],
    align="mid",
    color=["#d65f5f", "#5fba7d"],
)

[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.hide(axis="index")

[46]:
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 
[47]:

df.style.hide(["C", "D"], axis="columns")

[47]:
  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