csv 操作
[TOC]
读取 csv文件
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| import pandas as pd
data = pd.read_csv('预测数据.csv')
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查看前几行
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| headdata = data.head(5)
print(headdata)
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获取行
某行
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| # 第一行
print(data.ix[0,:])
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| apply_cnt 1.000000e+00
bbr_addressno 1.000000e+00
...
tbr_appntsex 1.000000e+00
tbr_lccont_cnt 1.000000e+00
tbr_marriage 7.000000e+00
tbr_year_prem 3.251000e+03
predictScoreColumnName 4.527450e-01
Name: 0, dtype: float64
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某几行
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| #获取第2/4/6行的数据
print(data.ix[[1,3,5],:])
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| apply_cnt bbr_addressno bbr_age bbr_is_has_medica bbr_marriage \
1 1.0 15.0 39.0 0.0 7.0
3 1.0 0.0 2.0 0.0 8.0
5 1.0 2.0 54.0 0.0 1.0
bbr_sex bd_amnt_sum bd_lccont_year dir_work_time dlr_bd_chuxian \
1 1.0 3131300.0 258.0 3031.0 1.0
3 0.0 180000.0 419.0 881.0 1.0
5 1.0 220000.0 181.0 227.0 1.0
... prt_accidentdate_del self_pol tabfeemoney \
1 ... 32.0 1.0 4602.10
3 ... 3.0 0.0 15090.84
5 ... -1.0 0.0 54939.69
tbr_accilccont_cnt tbr_age tbr_appntsex tbr_lccont_cnt tbr_marriage \
1 0.0 39.0 1.0 8.0 7.0
3 0.0 32.0 0.0 2.0 7.0
5 0.0 23.0 0.0 2.0 1.0
tbr_year_prem predictScoreColumnName
1 31436.0 0.448217
3 9300.0 0.693983
5 7048.0 0.332433
[3 rows x 30 columns]
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所有行
获取列
某列
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| print(data.ix[:, 'label'])
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| 0 0.0
1 0.0
2 0.0
3 0.0
4 1.0
...
18489 0.0
18490 0.0
18491 0.0
18492 0.0
Name: label, Length: 18493, dtype: float64
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某几列
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| print(data.ix[:, ['label','predictScoreColumnName','comment']])
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| label predictScoreColumnName comment
0 0.0 0.452745 NaN
1 0.0 0.448217 NaN
... ... ... ...
18491 0.0 0.359486 NaN
18492 0.0 0.317408 NaN
[18493 rows x 3 columns]
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数据统计
describe统计下数据量、标准值、平均值、最大值等
运行效果
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| apply_cnt bbr_addressno bbr_age bbr_is_has_medica
count 18493.000000 18493.000000 18493.00000 18493.000000
mean 1.300384 2.078246 36.21473 0.184502
std 0.909737 3.669211 14.59485 0.387904
min 1.000000 -1.000000 0.00000 0.000000
25% 1.000000 0.000000 31.00000 0.000000
50% 1.000000 1.000000 40.00000 0.000000
75% 1.000000 3.000000 46.00000 0.000000
max 30.000000 148.000000 71.00000 1.000000
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