numpickle 0.1.2.post5

Save and load numeric pandas data frames as numpy array and pickle their row and column names and types info for performance reasons.

Install

pip install numpickle

Usage

import pandas as pd
import numpickle as npl


# create example data frame with non-numeric and numeric columns
df = pd.DataFrame([[1, 2,'a'], [3, 4, 'b']])
df.columns = ["A", "B", "C"]
df.index = ["row1", "row2"]

df
#       A  B  C
# row1  1  2  a
# row2  3  4  b

df.dtypes
# A     int64
# B     int64
# C    object
# dtype: object




# save data frame as numpy array and pickle row and column names
# into helper pickle file "/home/user/test.npy.pckl"
npl.save_numpickle(df, "/home/user/test.npy")

# load the saved data
df_ = npl.load_numpickle("/home/user/test.npy")

df_
#       A  B  C
# row1  1  2  a
# row2  3  4  b


df_.dtypes
# A     int64
# B     int64
# C    object
# dtype: object

all(df == df_)
# True