pandas 将csv文件作为浮点数读取到pandas数据帧
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reading csv file to pandas dataframe as float
提问by doctorer
I have a .csvfile with strings in the top row and first column, with the rest of the data as floating point numbers. I want to read it into a dataframe with the first row and column as column names and index respectively, and all the floating values as float64.
我有一个.csv在顶行和第一列中包含字符串的文件，其余数据为浮点数。我想将它读入一个数据帧，第一行和第一列分别作为列名和索引，所有浮动值作为float64.
If I use df = pd.read_csv(filename,index_col=0)all the numeric values are left as strings.
如果我使用df = pd.read_csv(filename,index_col=0)所有数值都保留为字符串。
If I use df = pd.read_csv(filename, index_col=0, dtype=np.float64)I get an exception: ValueError: could not convert string to floatas it attempts to parse the first column as float.
如果我使用，df = pd.read_csv(filename, index_col=0, dtype=np.float64)我会得到一个异常：ValueError: could not convert string to float因为它试图将第一列解析为float.
There are a large number of columns, and i do not have the column names, so I don't want to identify each column for parsing as float; I want to parse every column exceptthe first one.
有很多列，我没有列名，所以我不想将每一列标识为float; 我想解析除第一列之外的每一列。
采纳答案by doctorer
The original code was correct
原始代码是正确的
df = pd.read_csv(filename,index_col=0)
but the .csvfile had been constructed incorrectly.
但该.csv文件的构造不正确。
As @juanpa.arrivillaga pointed out, pandas will infer the dtypeswithout any arguments, provided all the data in a column is of the same dtype. The columns were being interpreted as strings because although mostof the data was numeric, one row contained non-numeric data (actually dates). Removing this row from the .csvsolved the problem.
正如@juanpa.arrivillaga 指出的那样，dtypes只要列中的所有数据都是相同的，pandas 将在没有任何参数的情况下推断出dtype。这些列被解释为字符串，因为尽管大部分数据是数字，但一行包含非数字数据（实际上是日期）。从.csv解决了问题中删除这一行。
回答by Ragsparkz
Get the list of all column names, remove the first one. cast other columns.
获取所有列名的列表，删除第一个。投其他列。
cols = df.columns
cols.remove('fistcolumn')
for col in cols:
df[col] = df[col].astype(float)
