pythonpandasdataframecsvbigdata

Using usecols when specifying a multi-index header in Python Pandas

I have a huge data to read based on two headers, But when I am using multi-index approach I am unable to use 'usecols' in pandas dataframe.

enter image description here

When I am using

df = pd.read_csv(files, delimiter=' ', header=[0,1])

it is taking too much of time and memory.

Another approach I am trying to use is

df = pd.read_csv(files, delimiter=' ', usecols = ["80.375"])

it is taking only one column, rather it should take all the four columns with header '80.375'

Desired output

enter image description here

Please suggest any alternative approach

Thanks in advance

Solution

  • You can use two pass to extract data and headers.

    # read_csv common options
opts = {'sep': ' ', 'header': None}

# Extract headers, create MultiIndex
headers = pd.read_csv('data.csv', **opts, nrows=2)
mi = pd.MultiIndex.from_frame(headers.T)

# Keep desired columns
dti = [0, 1, 2]  # Year, Month, Day
cols = mi.get_locs([80.375]).tolist()

# Build dataframe
df = pd.read_csv('data.csv', **opts, skiprows=2, index_col=dti, usecols=dti+cols)
df.columns = mi[cols]
df = df.rename_axis(index=['Year', 'Month', 'Day'], columns=['Lvl1', 'Lvl2'])
df.index = pd.to_datetime(df.index.to_frame()).rename('DateTime')

    Output:

    >>> df
Lvl1       80.375                     
Lvl2       28.625 28.875 29.125 29.375
DateTime                              
2015-01-01     21     22     23     24
2015-01-02     31     32     33     34
2015-01-03     41     42     43     44
2015-01-04     51     52     53     54

    Input csv file:

       80.125 80.375 80.375 80.375 80.375 80.625
   28.875 28.625 28.875 29.125 29.375 28.875
2015 1 1 20 21 22 23 24 25
2015 1 2 30 31 32 33 34 35
2015 1 3 40 41 42 43 44 45
2015 1 4 50 51 52 53 54 55

    Update

    I need to convert the output in single header row.

    # Extract headers, create MultiIndex
headers = pd.read_csv('data.csv', sep=' ', header=None, nrows=2)
mi = pd.MultiIndex.from_frame(headers.T)

# Keep desired columns
dti_cols = [0, 1, 2]  # Year, Month, Day
dti_names = ['Year', 'Month', 'Day']

dat_cols = mi.get_locs([80.375]).tolist()
dat_names = mi[cols].to_flat_index().map(lambda x: f"{x[0]}_{x[1]}").tolist()

# Build dataframe
df = (pd.read_csv('data.csv', sep=' ', header=None, skiprows=2,
                  usecols=dti_cols+dat_cols, names=dti_names+dat_names,
                  parse_dates={'Date': ['Year', 'Month', 'Day']}))

    Output:

    >>> df
        Date  80.375_28.625  80.375_28.875  80.375_29.125  80.375_29.375
0 2015-01-01             21             22             23             24
1 2015-01-02             31             32             33             34
2 2015-01-03             41             42             43             44
3 2015-01-04             51             52             53             54