How to read HDF5 files in Python

Read HDF5

import h5py
filename = "file.hdf5"

with h5py.File(filename, "r") as f:
    # Print all root level object names (aka keys) 
    # these can be group or dataset names 
    print("Keys: %s" % f.keys())
    # get first object name/key; may or may NOT be a group
    a_group_key = list(f.keys())[0]

    # get the object type for a_group_key: usually group or dataset
    print(type(f[a_group_key])) 

    # If a_group_key is a group name, 
    # this gets the object names in the group and returns as a list
    data = list(f[a_group_key])

    # If a_group_key is a dataset name, 
    # this gets the dataset values and returns as a list
    data = list(f[a_group_key])
    # preferred methods to get dataset values:
    ds_obj = f[a_group_key]      # returns as a h5py dataset object
    ds_arr = f[a_group_key][()]  # returns as a numpy array

Write HDF5

import h5py

# Create random data
import numpy as np
data_matrix = np.random.uniform(-1, 1, size=(10, 3))

# Write data to HDF5
with h5py.File("file.hdf5", "w") as data_file:
    data_file.create_dataset("dataset_name", data=data_matrix)

See h5py docs for more information.

Alternatives

• JSON: Nice for writing human-readable data; VERY commonly used (read & write)
• CSV: Super simple format (read & write)
• pickle: A Python serialization format (read & write)
• MessagePack (Python package): More compact representation (read & write)
• HDF5 (Python package): Nice for matrices (read & write)
• XML: exists too *sigh* (read & write)

For your application, the following might be important:

• Support by other programming languages
• Reading / writing performance
• Compactness (file size)

See also: Comparison of data serialization formats

In case you are rather looking for a way to make configuration files, you might want to read my short article Configuration files in Python

Reading the file

import h5py

f = h5py.File(file_name, mode)

Studying the structure of the file by printing what HDF5 groups are present

for key in f.keys():
    print(key) #Names of the root level object names in HDF5 file - can be groups or datasets.
    print(type(f[key])) # get the object type: usually group or dataset

Extracting the data

#Get the HDF5 group; key needs to be a group name from above
group = f[key]

#Checkout what keys are inside that group.
for key in group.keys():
    print(key)

# This assumes group[some_key_inside_the_group] is a dataset, 
# and returns a np.array:
data = group[some_key_inside_the_group][()]
#Do whatever you want with data

#After you are done
f.close()

you can use Pandas.

import pandas as pd
pd.read_hdf(filename,key)