I have a CSV file (tmp.csv
) that looks like this:
x y z
bar 0.55 0.55 0.0
foo 0.3 0.4 0.1
qux 0.0 0.3 5.55
It was created with Pandas this way:
In [103]: df_dummy
Out[103]:
x y z
bar 0.55 0.55 0.00
foo 0.30 0.40 0.10
qux 0.00 0.30 5.55
In [104]: df_dummy.to_csv("tmp.csv",sep="\t")
What I want to do is to read that CSV into the same dataframe representation. I tried this but doesn't give what I want:
In [108]: pd.io.parsers.read_csv("tmp.csv",sep="\t")
Out[108]:
Unnamed: 0 x y z
0 bar 0.55 0.55 0.00
1 foo 0.30 0.40 0.10
2 qux 0.00 0.30 5.55
What's the right way to do it?
for reading a CSV file, call pd. read_csv(file_name, usecols=cols_list) with file_name as the name of the CSV file, delimiter as the delimiter, and cols_list as the list of specific columns to read from the CSV file. Call df[col] with df as the DataFrame from the previous step, and col as the column name to read.
nrows : This parameter allows you to control how many rows you want to load from the CSV file. It takes an integer specifying row count. B. skiprows : This parameter allows you to skip rows from the beginning of the file.
You can use index_col
parameter:
>>> pd.io.parsers.read_csv("tmp.csv",sep="\t",index_col=0)
x y z
bar 0.55 0.55 0.00
foo 0.30 0.40 0.10
qux 0.00 0.30 5.55
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