pandas.DataFrame.to_hdf   DataFrame.to_hdf(path_or_buf, key, mode='a', complevel=None, complib=None, append=False, format=None, index=True, min_itemsize=None, nan_rep=None, dropna=None, data_columns=None, errors='strict', encoding='UTF-8')[source]
 
Write the contained data to an HDF5 file using HDFStore. Hierarchical Data Format (HDF) is self-describing, allowing an application to interpret the structure and contents of a file with no outside information. One HDF file can hold a mix of related objects which can be accessed as a group or as individual objects. In order to add another DataFrame or Series to an existing HDF file please use append mode and a different a key.  Warning One can store a subclass of DataFrame or Series to HDF5, but the type of the subclass is lost upon storing.  For more information see the user guide.  Parameters 
 
path_or_buf:str or pandas.HDFStore


File path or HDFStore object.  
key:str


Identifier for the group in the store.  
mode:{‘a’, ‘w’, ‘r+’}, default ‘a’


Mode to open file:  ‘w’: write, a new file is created (an existing file with the same name would be deleted). ‘a’: append, an existing file is opened for reading and writing, and if the file does not exist it is created. ‘r+’: similar to ‘a’, but the file must already exist.   
complevel:{0-9}, default None


Specifies a compression level for data. A value of 0 or None disables compression.  
complib:{‘zlib’, ‘lzo’, ‘bzip2’, ‘blosc’}, default ‘zlib’


Specifies the compression library to be used. As of v0.20.2 these additional compressors for Blosc are supported (default if no compressor specified: ‘blosc:blosclz’): {‘blosc:blosclz’, ‘blosc:lz4’, ‘blosc:lz4hc’, ‘blosc:snappy’, ‘blosc:zlib’, ‘blosc:zstd’}. Specifying a compression library which is not available issues a ValueError.  
append:bool, default False


For Table formats, append the input data to the existing.  
format:{‘fixed’, ‘table’, None}, default ‘fixed’


Possible values:  ‘fixed’: Fixed format. Fast writing/reading. Not-appendable, nor searchable. ‘table’: Table format. Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data. If None, pd.get_option(‘io.hdf.default_format’) is checked, followed by fallback to “fixed”.   
errors:str, default ‘strict’


Specifies how encoding and decoding errors are to be handled. See the errors argument for open() for a full list of options.  
encoding:str, default “UTF-8”


min_itemsize:dict or int, optional


Map column names to minimum string sizes for columns.  
nan_rep:Any, optional


How to represent null values as str. Not allowed with append=True.  
data_columns:list of columns or True, optional


List of columns to create as indexed data columns for on-disk queries, or True to use all columns. By default only the axes of the object are indexed. See Query via data columns. Applicable only to format=’table’.      See also  read_hdf

Read from HDF file.  DataFrame.to_parquet

Write a DataFrame to the binary parquet format.  DataFrame.to_sql

Write to a SQL table.  DataFrame.to_feather

Write out feather-format for DataFrames.  DataFrame.to_csv

Write out to a csv file.    Examples 
>>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]},
...                   index=['a', 'b', 'c'])  
>>> df.to_hdf('data.h5', key='df', mode='w')  
  We can add another object to the same file: 
>>> s = pd.Series([1, 2, 3, 4])  
>>> s.to_hdf('data.h5', key='s')  
  Reading from HDF file: 
>>> pd.read_hdf('data.h5', 'df')  
A  B
a  1  4
b  2  5
c  3  6
>>> pd.read_hdf('data.h5', 's')  
0    1
1    2
2    3
3    4
dtype: int64