绝对基本的read_csv问题。
Date,Open Price,High Price,Low Price,Close Price,WAP,No.of Shares,No. of Trades,Total Turnover (Rs.),Deliverable Quantity,% Deli. Qty to Traded Qty,Spread High-Low,Spread Close-Open
28-February-2015,2270.00,2310.00,2258.00,2294.85,2279.192067772602217319,73422,8043,167342840.00,11556,15.74,52.00,24.85
27-February-2015,2267.25,2280.85,2258.00,2266.35,2269.239841485775122730,50721,4938,115098114.00,12297,24.24,22.85,-0.90
26-February-2015,2314.90,2314.90,2250.00,2259.50,2277.198324862194860047,69845,8403,159050917.00,22046,31.56,64.90,-55.40
25-February-2015,2290.00,2332.00,2278.35,2318.05,2315.100614216488163214,161995,10174,375034724.00,102972,63.56,53.65,28.05
24-February-2015,2276.05,2295.00,2258.00,2278.15,2281.058946240263344242,52251,7726,119187611.00,13292,25.44,37.00,2.10
23-February-2015,2303.95,2311.00,2253.25,2270.70,2281.912259219760108491,75951,7344,173313518.00,24969,32.88,57.75,-33.25
20-February-2015,2324.00,2335.20,2277.00,2284.30,2301.631421152326354478,79717,10233,183479152.00,23045,28.91,58.20,-39.70
19-February-2015,2304.00,2333.90,2292.00,2326.60,2321.485466301625211160,85835,8847,199264705.00,29728,34.63,41.90,22.60
18-February-2015,2284.00,2305.00,2261.10,2295.75,2282.060986778089405300,69884,6639,159479550.00,26665,38.16,43.90,11.75
16-February-2015,2281.00,2305.85,2266.00,2278.50,2284.961866239581019628,85541,10149,195457923.00,22164,25.91,39.85,-2.50
13-February-2015,2311.00,2324.90,2286.95,2296.40,2311.371235111317676864,109731,5570,253629077.00,69039,62.92,37.95,-14.60
12-February-2015,2280.00,2322.85,2275.00,2315.45,2301.372038211769425569,79766,9095,183571242.00,33981,42.60,47.85,35.45
11-February-2015,2275.00,2295.00,2258.25,2287.20,2279.587966250020639664,60563,7467,138058686.00,20058,33.12,36.75,12.20
10-February-2015,2244.90,2297.40,2225.00,2280.30,2269.562228214830293104,141656,13026,321497107.00,55577,39.23,72.40,35.40
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我正在尝试使用read_csv的以下变体在pandas数据帧中读取此数据。我只对两列感兴趣。
z = pd.read_csv('file.csv', parse_dates=True, index_col="Date", usecols=["Date", "Open Price", "Close Price"], names=["Date", "O", "C"], header=0)
我得到的是
O C
Date
2015-02-28 NaN NaN
2015-02-27 NaN NaN
2015-02-26 NaN NaN
2015-02-25 NaN NaN
2015-02-24 NaN NaN
Or
z = pd.read_csv('file.csv', parse_dates=True, index_col="Date", usecols=["Date", "Open", "Close"], names=["Date", "Open Price", "Close Price"], header=0)
结果是 -
Open Price Close Price
Date
2015-02-28 NaN NaN
2015-02-27 NaN NaN
2015-02-26 NaN NaN
2015-02-25 NaN NaN
我是否遗漏了一些基本内容或pandas 0.13.1的read_csv是否存在问题 - 我的版本是Debian Wheezy？
答案 0 :(得分：9)
你是对的，name属性有些奇怪。在我看来，你不能同时使用两者。您可以为CSV文件的每个列设置名称，也可以不设置名称。因此，当你没有拿走所有的列时，你似乎无法设置名称（usecols）
names : array-like
List of column names to use. If file contains no header row, then you should explicitly pass header=None
您可能已经知道了，但也可以重命名列。
import pandas as pd
from StringIO import StringIO
csv = r"""Date,Open Price,High Price,Low Price,Close Price,WAP,No.of Shares,No. of Trades,Total Turnover (Rs.),Deliverable Quantity,% Deli. Qty to Traded Qty,Spread High-Low,Spread Close-Open
28-February-2015,2270.00,2310.00,2258.00,2294.85,2279.192067772602217319,73422,8043,167342840.00,11556,15.74,52.00,24.85
27-February-2015,2267.25,2280.85,2258.00,2266.35,2269.239841485775122730,50721,4938,115098114.00,12297,24.24,22.85,-0.90
26-February-2015,2314.90,2314.90,2250.00,2259.50,2277.198324862194860047,69845,8403,159050917.00,22046,31.56,64.90,-55.40
25-February-2015,2290.00,2332.00,2278.35,2318.05,2315.100614216488163214,161995,10174,375034724.00,102972,63.56,53.65,28.05
24-February-2015,2276.05,2295.00,2258.00,2278.15,2281.058946240263344242,52251,7726,119187611.00,13292,25.44,37.00,2.10
23-February-2015,2303.95,2311.00,2253.25,2270.70,2281.912259219760108491,75951,7344,173313518.00,24969,32.88,57.75,-33.25
20-February-2015,2324.00,2335.20,2277.00,2284.30,2301.631421152326354478,79717,10233,183479152.00,23045,28.91,58.20,-39.70
19-February-2015,2304.00,2333.90,2292.00,2326.60,2321.485466301625211160,85835,8847,199264705.00,29728,34.63,41.90,22.60
18-February-2015,2284.00,2305.00,2261.10,2295.75,2282.060986778089405300,69884,6639,159479550.00,26665,38.16,43.90,11.75
16-February-2015,2281.00,2305.85,2266.00,2278.50,2284.961866239581019628,85541,10149,195457923.00,22164,25.91,39.85,-2.50
13-February-2015,2311.00,2324.90,2286.95,2296.40,2311.371235111317676864,109731,5570,253629077.00,69039,62.92,37.95,-14.60
12-February-2015,2280.00,2322.85,2275.00,2315.45,2301.372038211769425569,79766,9095,183571242.00,33981,42.60,47.85,35.45
11-February-2015,2275.00,2295.00,2258.25,2287.20,2279.587966250020639664,60563,7467,138058686.00,20058,33.12,36.75,12.20
10-February-2015,2244.90,2297.40,2225.00,2280.30,2269.562228214830293104,141656,13026,321497107.00,55577,39.23,72.40,35.40"""
df = pd.read_csv(StringIO(csv),
usecols=["Date", "Open Price", "Close Price"],
header=0)
df.columns = ['Date', 'O', 'C']
df
输出：
Date O C
0 28-February-2015 2270.00 2294.85
1 27-February-2015 2267.25 2266.35
2 26-February-2015 2314.90 2259.50
3 25-February-2015 2290.00 2318.05
4 24-February-2015 2276.05 2278.15
5 23-February-2015 2303.95 2270.70
6 20-February-2015 2324.00 2284.30
7 19-February-2015 2304.00 2326.60
8 18-February-2015 2284.00 2295.75
9 16-February-2015 2281.00 2278.50
10 13-February-2015 2311.00 2296.40
11 12-February-2015 2280.00 2315.45
12 11-February-2015 2275.00 2287.20
13 10-February-2015 2244.90 2280.30
答案 1 :(得分：1)
根据文档，您的使用条款列表应该是新的名称列表的子集
usecols : list-like or callable, default None
Return a subset of the columns. If list-like, all elements must either
be positional (i.e. integer indices into the document columns) or strings
that correspond to column names provided either by the user in `names` or
inferred from the document header row(s).
csv示例
"OLD1", "OLD2", "OLD3"
1,2,3
4,5,6
重命名OLDX-> NEWX并仅使用NEW2 + NEW3的代码
import pandas as pd
d = pd.read_csv('test.csv', header=0, names=['NEW1', 'NEW2', 'NEW3'], usecols=['NEW2', 'NEW3'])
输出
NEW2 NEW3
0 2 3
1 5 6
注意：：即使上述操作按预期工作，更改engine='python'时也会出现问题
d = pd.read_csv('test.csv', header=0, engine='python',
names=['NEW1', 'NEW2', 'NEW3'], usecols=['NEW2', 'NEW3'])
ValueError: Number of passed names did not match number of header fields in the file
设置了header=None和skiprows=[0,]的解决方法：
d = pd.read_csv('test.csv', header=None, skiprows=[0,], engine='python', names=['NEW1', 'NEW2', 'NEW3'], usecols=['NEW2', 'NEW3'])
输出
NEW2 NEW3
0 2 3
1 5 6
熊猫版本：0.23.4