显示设置

import pandas as pd

df = pd.read_csv('../../datasets/titanic/test.csv')
df.head(80)
PassengerId Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
0 892 3 Kelly, Mr. James male 34.5 0 0 330911 7.8292 NaN Q
1 893 3 Wilkes, Mrs. James (Ellen Needs) female 47.0 1 0 363272 7.0000 NaN S
2 894 2 Myles, Mr. Thomas Francis male 62.0 0 0 240276 9.6875 NaN Q
3 895 3 Wirz, Mr. Albert male 27.0 0 0 315154 8.6625 NaN S
4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female 22.0 1 1 3101298 12.2875 NaN S
... ... ... ... ... ... ... ... ... ... ... ...
75 967 1 Keeping, Mr. Edwin male 32.5 0 0 113503 211.5000 C132 C
76 968 3 Miles, Mr. Frank male NaN 0 0 359306 8.0500 NaN S
77 969 1 Cornell, Mrs. Robert Clifford (Malvina Helen L... female 55.0 2 0 11770 25.7000 C101 S
78 970 2 Aldworth, Mr. Charles Augustus male 30.0 0 0 248744 13.0000 NaN S
79 971 3 Doyle, Miss. Elizabeth female 24.0 0 0 368702 7.7500 NaN Q

80 rows × 11 columns

上面的数据过多，于是显示不了就用 … 代替 如何设置显示设置？

最大显示样本数
pd.get_option('display.max_rows')     # 显示能展示的最大样本数量

60

pd.set_option('display.max_rows', 80)   # 设置其最大显示容量为 80

df.head(80)          # 一旦超过最大容量，就会被省略 只显示头尾
PassengerId Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
0 892 3 Kelly, Mr. James male 34.5 0 0 330911 7.8292 NaN Q
1 893 3 Wilkes, Mrs. James (Ellen Needs) female 47.0 1 0 363272 7.0000 NaN S
2 894 2 Myles, Mr. Thomas Francis male 62.0 0 0 240276 9.6875 NaN Q
3 895 3 Wirz, Mr. Albert male 27.0 0 0 315154 8.6625 NaN S
4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female 22.0 1 1 3101298 12.2875 NaN S
5 897 3 Svensson, Mr. Johan Cervin male 14.0 0 0 7538 9.2250 NaN S
6 898 3 Connolly, Miss. Kate female 30.0 0 0 330972 7.6292 NaN Q
7 899 2 Caldwell, Mr. Albert Francis male 26.0 1 1 248738 29.0000 NaN S
8 900 3 Abrahim, Mrs. Joseph (Sophie Halaut Easu) female 18.0 0 0 2657 7.2292 NaN C
9 901 3 Davies, Mr. John Samuel male 21.0 2 0 A/4 48871 24.1500 NaN S
10 902 3 Ilieff, Mr. Ylio male NaN 0 0 349220 7.8958 NaN S
11 903 1 Jones, Mr. Charles Cresson male 46.0 0 0 694 26.0000 NaN S
12 904 1 Snyder, Mrs. John Pillsbury (Nelle Stevenson) female 23.0 1 0 21228 82.2667 B45 S
13 905 2 Howard, Mr. Benjamin male 63.0 1 0 24065 26.0000 NaN S
14 906 1 Chaffee, Mrs. Herbert Fuller (Carrie Constance... female 47.0 1 0 W.E.P. 5734 61.1750 E31 S
15 907 2 del Carlo, Mrs. Sebastiano (Argenia Genovesi) female 24.0 1 0 SC/PARIS 2167 27.7208 NaN C
16 908 2 Keane, Mr. Daniel male 35.0 0 0 233734 12.3500 NaN Q
17 909 3 Assaf, Mr. Gerios male 21.0 0 0 2692 7.2250 NaN C
18 910 3 Ilmakangas, Miss. Ida Livija female 27.0 1 0 STON/O2. 3101270 7.9250 NaN S
19 911 3 Assaf Khalil, Mrs. Mariana (Miriam")" female 45.0 0 0 2696 7.2250 NaN C
20 912 1 Rothschild, Mr. Martin male 55.0 1 0 PC 17603 59.4000 NaN C
21 913 3 Olsen, Master. Artur Karl male 9.0 0 1 C 17368 3.1708 NaN S
22 914 1 Flegenheim, Mrs. Alfred (Antoinette) female NaN 0 0 PC 17598 31.6833 NaN S
23 915 1 Williams, Mr. Richard Norris II male 21.0 0 1 PC 17597 61.3792 NaN C
24 916 1 Ryerson, Mrs. Arthur Larned (Emily Maria Borie) female 48.0 1 3 PC 17608 262.3750 B57 B59 B63 B66 C
25 917 3 Robins, Mr. Alexander A male 50.0 1 0 A/5. 3337 14.5000 NaN S
26 918 1 Ostby, Miss. Helene Ragnhild female 22.0 0 1 113509 61.9792 B36 C
27 919 3 Daher, Mr. Shedid male 22.5 0 0 2698 7.2250 NaN C
28 920 1 Brady, Mr. John Bertram male 41.0 0 0 113054 30.5000 A21 S
29 921 3 Samaan, Mr. Elias male NaN 2 0 2662 21.6792 NaN C
30 922 2 Louch, Mr. Charles Alexander male 50.0 1 0 SC/AH 3085 26.0000 NaN S
31 923 2 Jefferys, Mr. Clifford Thomas male 24.0 2 0 C.A. 31029 31.5000 NaN S
32 924 3 Dean, Mrs. Bertram (Eva Georgetta Light) female 33.0 1 2 C.A. 2315 20.5750 NaN S
33 925 3 Johnston, Mrs. Andrew G (Elizabeth Lily" Watson)" female NaN 1 2 W./C. 6607 23.4500 NaN S
34 926 1 Mock, Mr. Philipp Edmund male 30.0 1 0 13236 57.7500 C78 C
35 927 3 Katavelas, Mr. Vassilios (Catavelas Vassilios")" male 18.5 0 0 2682 7.2292 NaN C
36 928 3 Roth, Miss. Sarah A female NaN 0 0 342712 8.0500 NaN S
37 929 3 Cacic, Miss. Manda female 21.0 0 0 315087 8.6625 NaN S
38 930 3 Sap, Mr. Julius male 25.0 0 0 345768 9.5000 NaN S
39 931 3 Hee, Mr. Ling male NaN 0 0 1601 56.4958 NaN S
40 932 3 Karun, Mr. Franz male 39.0 0 1 349256 13.4167 NaN C
41 933 1 Franklin, Mr. Thomas Parham male NaN 0 0 113778 26.5500 D34 S
42 934 3 Goldsmith, Mr. Nathan male 41.0 0 0 SOTON/O.Q. 3101263 7.8500 NaN S
43 935 2 Corbett, Mrs. Walter H (Irene Colvin) female 30.0 0 0 237249 13.0000 NaN S
44 936 1 Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons) female 45.0 1 0 11753 52.5542 D19 S
45 937 3 Peltomaki, Mr. Nikolai Johannes male 25.0 0 0 STON/O 2. 3101291 7.9250 NaN S
46 938 1 Chevre, Mr. Paul Romaine male 45.0 0 0 PC 17594 29.7000 A9 C
47 939 3 Shaughnessy, Mr. Patrick male NaN 0 0 370374 7.7500 NaN Q
48 940 1 Bucknell, Mrs. William Robert (Emma Eliza Ward) female 60.0 0 0 11813 76.2917 D15 C
49 941 3 Coutts, Mrs. William (Winnie Minnie" Treanor)" female 36.0 0 2 C.A. 37671 15.9000 NaN S
50 942 1 Smith, Mr. Lucien Philip male 24.0 1 0 13695 60.0000 C31 S
51 943 2 Pulbaum, Mr. Franz male 27.0 0 0 SC/PARIS 2168 15.0333 NaN C
52 944 2 Hocking, Miss. Ellen Nellie"" female 20.0 2 1 29105 23.0000 NaN S
53 945 1 Fortune, Miss. Ethel Flora female 28.0 3 2 19950 263.0000 C23 C25 C27 S
54 946 2 Mangiavacchi, Mr. Serafino Emilio male NaN 0 0 SC/A.3 2861 15.5792 NaN C
55 947 3 Rice, Master. Albert male 10.0 4 1 382652 29.1250 NaN Q
56 948 3 Cor, Mr. Bartol male 35.0 0 0 349230 7.8958 NaN S
57 949 3 Abelseth, Mr. Olaus Jorgensen male 25.0 0 0 348122 7.6500 F G63 S
58 950 3 Davison, Mr. Thomas Henry male NaN 1 0 386525 16.1000 NaN S
59 951 1 Chaudanson, Miss. Victorine female 36.0 0 0 PC 17608 262.3750 B61 C
60 952 3 Dika, Mr. Mirko male 17.0 0 0 349232 7.8958 NaN S
61 953 2 McCrae, Mr. Arthur Gordon male 32.0 0 0 237216 13.5000 NaN S
62 954 3 Bjorklund, Mr. Ernst Herbert male 18.0 0 0 347090 7.7500 NaN S
63 955 3 Bradley, Miss. Bridget Delia female 22.0 0 0 334914 7.7250 NaN Q
64 956 1 Ryerson, Master. John Borie male 13.0 2 2 PC 17608 262.3750 B57 B59 B63 B66 C
65 957 2 Corey, Mrs. Percy C (Mary Phyllis Elizabeth Mi... female NaN 0 0 F.C.C. 13534 21.0000 NaN S
66 958 3 Burns, Miss. Mary Delia female 18.0 0 0 330963 7.8792 NaN Q
67 959 1 Moore, Mr. Clarence Bloomfield male 47.0 0 0 113796 42.4000 NaN S
68 960 1 Tucker, Mr. Gilbert Milligan Jr male 31.0 0 0 2543 28.5375 C53 C
69 961 1 Fortune, Mrs. Mark (Mary McDougald) female 60.0 1 4 19950 263.0000 C23 C25 C27 S
70 962 3 Mulvihill, Miss. Bertha E female 24.0 0 0 382653 7.7500 NaN Q
71 963 3 Minkoff, Mr. Lazar male 21.0 0 0 349211 7.8958 NaN S
72 964 3 Nieminen, Miss. Manta Josefina female 29.0 0 0 3101297 7.9250 NaN S
73 965 1 Ovies y Rodriguez, Mr. Servando male 28.5 0 0 PC 17562 27.7208 D43 C
74 966 1 Geiger, Miss. Amalie female 35.0 0 0 113503 211.5000 C130 C
75 967 1 Keeping, Mr. Edwin male 32.5 0 0 113503 211.5000 C132 C
76 968 3 Miles, Mr. Frank male NaN 0 0 359306 8.0500 NaN S
77 969 1 Cornell, Mrs. Robert Clifford (Malvina Helen L... female 55.0 2 0 11770 25.7000 C101 S
78 970 2 Aldworth, Mr. Charles Augustus male 30.0 0 0 248744 13.0000 NaN S
79 971 3 Doyle, Miss. Elizabeth female 24.0 0 0 368702 7.7500 NaN Q
属性的最大显示列数
pd.get_option('display.max_columns')   # 列的最大显示容量

20

pd.DataFrame(columns=range(25))
0 1 2 3 4 5 6 7 8 9 ... 15 16 17 18 19 20 21 22 23 24

0 rows × 25 columns

pd.set_option('display.max_columns', 25)
pd.DataFrame(columns=range(25))
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
数据的最大显示位数
pd.get_option('display.max_colwidth')   # 一个数据最多显示字符数

50

pd.Series(index=['a'], data=['t' * 60])

a    tttttttttttttttttttttttttttttttttttttttttttttt...
dtype: object

pd.set_option('display.max_colwidth', 61)
pd.Series(index=['a'], data=['t' * 60])

a    tttttttttttttttttttttttttttttttttttttttttttttttttttttttttttt
dtype: object
浮点数的精度
pd.get_option('display.precision')    # 小数点后最多能显示多少位

6

pd.Series(data=[1.6383822122])

0    1.638382
dtype: float64

pd.set_option('display.precision', 15)
pd.Series(data=[1.6383822122])

0    1.6383822122
dtype: float64

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