Pandas groupby method
<p>l have a 41 year dataset and l would like to do some statistical calculations by using a Pandas module. However, l have a lack of Pandas knowledge.
here is an example csv file dataset:</p>

<pre><code>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
</code></pre>

<p>here is my code:</p>

<pre><code>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')
</code></pre>

<p>but values under columns of PCP seems stored as string. 
here is example output for <code>pcp1</code>:</p>

<pre><code>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...  
</code></pre>

<p>how can l solve that problem?  </p>

<p>Do not specify <code>header=None</code> in your <code>read_csv</code> calls. You are telling the function that there is no header row in the data, when according to the sample data you posted above, the first row of the file is a header. So it treats that first header row as data, thus mixing values like <code>pcp1</code> and <code>0.431</code>, and causing all the columns to be interpreted as strings. </p>

python pandas