我有一个长达41年的数据集,希望使用Pandas模块进行一些统计计算。然而,我对Pandas的知识欠缺。以下是一个CSV文件数据集的示例:
date day month year pcp1 pcp2 pcp3 pcp4 pcp5 pcp6
1.01.1979 1 1 1979 0.431 2.167 9.375 0.431 2.167 9.375
2.01.1979 2 1 1979 1.216 2.583 9.162 1.216 2.583 9.162
3.01.1979 3 1 1979 4.041 9.373 23.169 4.041 9.373 23.169
4.01.1979 4 1 1979 1.799 3.866 8.286 1.799 3.866 8.286
5.01.1979 5 1 1979 0.003 0.051 0.342 0.003 0.051 0.342
6.01.1979 6 1 1979 2.345 3.777 7.483 2.345 3.777 7.483
7.01.1979 7 1 1979 0.017 0.031 0.173 0.017 0.031 0.173
8.01.1979 8 1 1979 5.061 5.189 43.313 5.061 5.189 43.313
这是我的代码:
import numpy as np
import pandas as pd
import csv
filename="output813b.csv"
cols = ["date","year","month","day" ,"pcp1","pcp2","pcp3","pcp4","pcp5","pcp6"]
data1=pd.read_csv(filename,sep=',', header=None,names=cols,usecols=range(1,9))
colmns_needed=["month" ,"pcp1","pcp2","pcp3","pcp4","pcp5","pcp6"]
data2=pd.read_csv(filename,sep=',', header=None,names=colmns_needed)
mm=data2.groupby("month")
print(mm.sum())
print('\n')
但是 PCP 列下的值似乎存储为字符串。以下是 pcp1
的示例输出:
Month pcp1
1 0.4310.4720000.91800000.01011.63904.65900.5780...
10 00.1500000000.027000.02400.1630.9610000000.017...
11 00.4940000000000.0480.003012.26200000003.612.9...
12 0.1890.0760.47000000000.08800.1080.26107.15000...
13 00.06500.1060.00700000050.6207.1510.0860.1487....
14 0000.64200000000.017025.5910.93400.04500000000...
15 0.742000.0720000000000.32500000000002.9877.512...
16 6.43900000000000.38103.986000000000033.5534.76...
17 0.0890000.2750000.555001.9230.562.9130.1360000...
18 3.28200000000.024000.656002.1750000000008.2434...
19 1.28200000000000000.0070000000007.0383.0450.17...
2 1.2160.1050000000010.4690.2092.9700.0415.6062....
20 00.4960.05100000000000.3550.1582.8530.04600000...
21 00000000000002.69903.5190.13000002.830.5151.09...
22 0000000007.19600000000000001.4421.76500.04500....
23 0000000008.168000.02100000000000.1083.8760.968...
我该如何解决这个问题?
data2.loc[:, 'pcp1':'pcp6'] = data2.loc[:, 'pcp1':'pcp6'].astype('float')
- ayhandata2 = pd.read_csv(filename)
就足够了(不需要再传递列名)。 - ayhan