I am getting the following error after using pandas.HDFStore().append()
ValueError: Trying to store a string with len [150] in [values_block_0] column but this column has a limit of [127]!
Consider using min_itemsize to preset the sizes on these columns
I am creating a pandas DataFrame and appending it to the HDF5 file as follows:
import pandas as pd
store = pd.HDFStore("test1.h5", mode='w')
hdf_key = "one_key"
columns = ["col1", "col2", ... ]
df = pd.Dataframe(...)
df.col1 = df.col1.astype(str)
df.col2 = df.col2astype(int)
df.col3 = df.col3astype(str)
....
store.append(hdf_key, df, data_column=columns, index=False)
I get the error above: "ValueError: Trying to store a string with len [150] in [values_block_0] column but this column has a limit of [127]!"
Afterwards, I execute the code:
store.get_storer(hdf_key).table.description
which outputs
{
"index": Int64Col(shape=(), dflt=0, pos=0),
"values_block_0": StringCol(itemsize=127, shape=(5,), dflt=b'', pos=1),
"values_block_1": Int64Col(shape=(5,), dflt=0, pos=2),
"col1": StringCol(itemsize=20, shape=(), dflt=b'', pos=3),
"col2": StringCol(itemsize=39, shape=(), dflt=b'', pos=4)}
What are values_block_0 and values_block_1?
So, following this StackOverflow Pandas pytable: how to specify min_itemsize of the elements of a MultiIndex , I tried
store.append(hdf_key, df, data_column=columns, index=False, min_itemsize={"values_block_0":250})
This doesn't work though---now I get this error:
ValueError: Trying to store a string with len [250] in [values_block_0] column but this column has a limit of [127]!
Consider using min_itemsize to preset the sizes on these columns
What am I doing wrong?
EDIT: This code produces the error ValueError: min_itemsize has the key [values_block_0] which is not an axis or data_column from filename.py
import pandas as pd
store = pd.HDFStore("test1.h5", mode='w')
hdf_key = "one_key"
my_columns = ["col1", "col2", ... ]
df = pd.Dataframe(...)
df.col1 = df.col1.astype(str)
df.col2 = df.col2astype(int)
df.col3 = df.col3astype(str)
....
store.append(hdf_key, df, data_column=my_columns, index=False, min_itemsize={"values_block_0":350})
Here is the full error:
(python-3) -bash:1008 $ python filename.py
Traceback (most recent call last):
File "filename.py", line 50, in <module>
store.append(hdf_key, dicts_into_df, data_column=my_columns, index=False, min_itemsize={'values_block_0':350})
File "/path/lib/python-3/lib/python3.5/site-packages/pandas/io/pytables.py", line 970, in append
**kwargs)
File "/path/lib/python-3/lib/python3.5/site-packages/pandas/io/pytables.py", line 1315, in _write_to_group
s.write(obj=value, append=append, complib=complib, **kwargs)
File "/path/lib/python-3/lib/python3.5/site-packages/pandas/io/pytables.py", line 4263, in write
obj=obj, data_columns=data_columns, **kwargs)
File "/path/lib/python-3/lib/python3.5/site-packages/pandas/io/pytables.py", line 3853, in write
**kwargs)
File "/path/lib/python-3/lib/python3.5/site-packages/pandas/io/pytables.py", line 3535, in create_axes
self.validate_min_itemsize(min_itemsize)
File "/path/lib/python-3/lib/python3.5/site-packages/pandas/io/pytables.py", line 3174, in validate_min_itemsize
"data_column" % k)
ValueError: min_itemsize has the key [values_block_0] which is not an axis or data_column
UPDATE:
you have misspelled data_columns parameter: data_column - it should be data_columns. As a result you didn't have any indexed columns in your HDF Store and HDF store added values_block_X:
In [70]: store = pd.HDFStore(r'D:\temp\.data\my_test.h5')
misspelled parameters will be ignored:
In [71]: store.append('no_idx_wrong_dc', df, data_column=df.columns, index=False)
In [72]: store.get_storer('no_idx_wrong_dc').table
Out[72]:
/no_idx_wrong_dc/table (Table(10,)) ''
description := {
"index": Int64Col(shape=(), dflt=0, pos=0),
"values_block_0": Float64Col(shape=(1,), dflt=0.0, pos=1),
"values_block_1": Int64Col(shape=(1,), dflt=0, pos=2),
"values_block_2": StringCol(itemsize=30, shape=(1,), dflt=b'', pos=3)}
byteorder := 'little'
chunkshape := (1213,)
is the same as the following:
In [73]: store.append('no_idx_no_dc', df, index=False)
In [74]: store.get_storer('no_idx_no_dc').table
Out[74]:
/no_idx_no_dc/table (Table(10,)) ''
description := {
"index": Int64Col(shape=(), dflt=0, pos=0),
"values_block_0": Float64Col(shape=(1,), dflt=0.0, pos=1),
"values_block_1": Int64Col(shape=(1,), dflt=0, pos=2),
"values_block_2": StringCol(itemsize=30, shape=(1,), dflt=b'', pos=3)}
byteorder := 'little'
chunkshape := (1213,)
let's spell it correctly:
In [75]: store.append('no_idx_dc', df, data_columns=df.columns, index=False)
In [76]: store.get_storer('no_idx_dc').table
Out[76]:
/no_idx_dc/table (Table(10,)) ''
description := {
"index": Int64Col(shape=(), dflt=0, pos=0),
"value": Float64Col(shape=(), dflt=0.0, pos=1),
"count": Int64Col(shape=(), dflt=0, pos=2),
"s": StringCol(itemsize=30, shape=(), dflt=b'', pos=3)}
byteorder := 'little'
chunkshape := (1213,)
OLD Answer:
AFAIK you can effectively set the min_itemsize parameter on the first append only.
Demo:
In [33]: df
Out[33]:
num s
0 11 aaaaaaaaaaaaaaaa
1 12 bbbbbbbbbbbbbb
2 13 ccccccccccccc
3 14 ddddddddddd
In [34]: store = pd.HDFStore(r'D:\temp\.data\my_test.h5')
In [35]: store.append('test_1', df, data_columns=True)
In [36]: store.get_storer('test_1').table.description
Out[36]:
{
"index": Int64Col(shape=(), dflt=0, pos=0),
"num": Int64Col(shape=(), dflt=0, pos=1),
"s": StringCol(itemsize=16, shape=(), dflt=b'', pos=2)}
In [37]: df.loc[4] = [15, 'X'*200]
In [38]: df
Out[38]:
num s
0 11 aaaaaaaaaaaaaaaa
1 12 bbbbbbbbbbbbbb
2 13 ccccccccccccc
3 14 ddddddddddd
4 15 XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX...
In [39]: store.append('test_1', df, data_columns=True)
...
skipped
...
ValueError: Trying to store a string with len [200] in [s] column but
this column has a limit of [16]!
Consider using min_itemsize to preset the sizes on these columns
now using min_itemsize, but still appending to the existing store object:
In [40]: store.append('test_1', df, data_columns=True, min_itemsize={'s':250})
...
skipped
...
ValueError: Trying to store a string with len [250] in [s] column but
this column has a limit of [16]!
Consider using min_itemsize to preset the sizes on these columns
The following works if we will create a new object in our store:
In [41]: store.append('test_2', df, data_columns=True, min_itemsize={'s':250})
Check column sizes:
In [42]: store.get_storer('test_2').table.description
Out[42]:
{
"index": Int64Col(shape=(), dflt=0, pos=0),
"num": Int64Col(shape=(), dflt=0, pos=1),
"s": StringCol(itemsize=250, shape=(), dflt=b'', pos=2)}