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json.py
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import numpy as np
from pandas import DataFrame, concat, date_range, read_json, timedelta_range
import pandas.util.testing as tm
from ..pandas_vb_common import BaseIO
class ReadJSON(BaseIO):
fname = "__test__.json"
params = (["split", "index", "records"], ["int", "datetime"])
param_names = ["orient", "index"]
def setup(self, orient, index):
N = 100000
indexes = {
"int": np.arange(N),
"datetime": date_range("20000101", periods=N, freq="H"),
}
df = DataFrame(
np.random.randn(N, 5),
columns=["float_{}".format(i) for i in range(5)],
index=indexes[index],
)
df.to_json(self.fname, orient=orient)
def time_read_json(self, orient, index):
read_json(self.fname, orient=orient)
class ReadJSONLines(BaseIO):
fname = "__test_lines__.json"
params = ["int", "datetime"]
param_names = ["index"]
def setup(self, index):
N = 100000
indexes = {
"int": np.arange(N),
"datetime": date_range("20000101", periods=N, freq="H"),
}
df = DataFrame(
np.random.randn(N, 5),
columns=["float_{}".format(i) for i in range(5)],
index=indexes[index],
)
df.to_json(self.fname, orient="records", lines=True)
def time_read_json_lines(self, index):
read_json(self.fname, orient="records", lines=True)
def time_read_json_lines_concat(self, index):
concat(read_json(self.fname, orient="records", lines=True, chunksize=25000))
def peakmem_read_json_lines(self, index):
read_json(self.fname, orient="records", lines=True)
def peakmem_read_json_lines_concat(self, index):
concat(read_json(self.fname, orient="records", lines=True, chunksize=25000))
class ToJSON(BaseIO):
fname = "__test__.json"
params = [
["split", "columns", "index", "values", "records"],
["df", "df_date_idx", "df_td_int_ts", "df_int_floats", "df_int_float_str"],
]
param_names = ["orient", "frame"]
def setup(self, orient, frame):
N = 10 ** 5
ncols = 5
index = date_range("20000101", periods=N, freq="H")
timedeltas = timedelta_range(start=1, periods=N, freq="s")
datetimes = date_range(start=1, periods=N, freq="s")
ints = np.random.randint(100000000, size=N)
floats = np.random.randn(N)
strings = tm.makeStringIndex(N)
self.df = DataFrame(np.random.randn(N, ncols), index=np.arange(N))
self.df_date_idx = DataFrame(np.random.randn(N, ncols), index=index)
self.df_td_int_ts = DataFrame(
{
"td_1": timedeltas,
"td_2": timedeltas,
"int_1": ints,
"int_2": ints,
"ts_1": datetimes,
"ts_2": datetimes,
},
index=index,
)
self.df_int_floats = DataFrame(
{
"int_1": ints,
"int_2": ints,
"int_3": ints,
"float_1": floats,
"float_2": floats,
"float_3": floats,
},
index=index,
)
self.df_int_float_str = DataFrame(
{
"int_1": ints,
"int_2": ints,
"float_1": floats,
"float_2": floats,
"str_1": strings,
"str_2": strings,
},
index=index,
)
def time_to_json(self, orient, frame):
getattr(self, frame).to_json(self.fname, orient=orient)
def peakmem_to_json(self, orient, frame):
getattr(self, frame).to_json(self.fname, orient=orient)
def time_to_json_wide(self, orient, frame):
base_df = getattr(self, frame).copy()
df = concat([base_df.iloc[:100]] * 1000, ignore_index=True, axis=1)
df.to_json(self.fname, orient=orient)
def peakmem_to_json_wide(self, orient, frame):
base_df = getattr(self, frame).copy()
df = concat([base_df.iloc[:100]] * 1000, ignore_index=True, axis=1)
df.to_json(self.fname, orient=orient)
class ToJSONLines(BaseIO):
fname = "__test__.json"
def setup(self):
N = 10 ** 5
ncols = 5
index = date_range("20000101", periods=N, freq="H")
timedeltas = timedelta_range(start=1, periods=N, freq="s")
datetimes = date_range(start=1, periods=N, freq="s")
ints = np.random.randint(100000000, size=N)
floats = np.random.randn(N)
strings = tm.makeStringIndex(N)
self.df = DataFrame(np.random.randn(N, ncols), index=np.arange(N))
self.df_date_idx = DataFrame(np.random.randn(N, ncols), index=index)
self.df_td_int_ts = DataFrame(
{
"td_1": timedeltas,
"td_2": timedeltas,
"int_1": ints,
"int_2": ints,
"ts_1": datetimes,
"ts_2": datetimes,
},
index=index,
)
self.df_int_floats = DataFrame(
{
"int_1": ints,
"int_2": ints,
"int_3": ints,
"float_1": floats,
"float_2": floats,
"float_3": floats,
},
index=index,
)
self.df_int_float_str = DataFrame(
{
"int_1": ints,
"int_2": ints,
"float_1": floats,
"float_2": floats,
"str_1": strings,
"str_2": strings,
},
index=index,
)
def time_floats_with_int_idex_lines(self):
self.df.to_json(self.fname, orient="records", lines=True)
def time_floats_with_dt_index_lines(self):
self.df_date_idx.to_json(self.fname, orient="records", lines=True)
def time_delta_int_tstamp_lines(self):
self.df_td_int_ts.to_json(self.fname, orient="records", lines=True)
def time_float_int_lines(self):
self.df_int_floats.to_json(self.fname, orient="records", lines=True)
def time_float_int_str_lines(self):
self.df_int_float_str.to_json(self.fname, orient="records", lines=True)
class ToJSONMem:
def setup_cache(self):
df = DataFrame([[1]])
frames = {"int": df, "float": df.astype(float)}
return frames
def peakmem_int(self, frames):
df = frames["int"]
for _ in range(100_000):
df.to_json()
def peakmem_float(self, frames):
df = frames["float"]
for _ in range(100_000):
df.to_json()
from ..pandas_vb_common import setup