目录
info
ataFram对象调用 info() 时会显示 DataFrame 的内存使用情况（包括索引）。
例如，调用 info() 时会显示下面的 DataFrame 的内存使用情况：
import pandas as pd
import numpy as np
dtypes = [
"int8",
"uint8",
"int16",
"int32",
"int64",
"float64",
"datetime64[ns]",
"timedelta64[ns]",
"complex128",
"object",
"bool",
]
n = 5000
data = {"col_"+t: np.random.randint(100, size=n).astype(t) for t in dtypes}
df = pd.DataFrame(data)
df["categorical"] = df["col_object"].astype("category")
df.info()
output
RangeIndex: 5000 entries, 0 to 4999
Data columns (total 12 columns):
# Column Non-Null Count Dtype
0 col_int8 5000 non-null int8
1 col_uint8 5000 non-null uint8
2 col_int16 5000 non-null int16
3 col_int32 5000 non-null int32
4 col_int64 5000 non-null int64
5 col_float64 5000 non-null float64
6 col_datetime64[ns] 5000 non-null datetime64[ns]
7 col_timedelta64[ns] 5000 non-null timedelta64[ns]
8 col_complex128 5000 non-null complex128
9 col_object 5000 non-null object
10 col_bool 5000 non-null bool
11 categorical 5000 non-null category
dtypes: bool(1), category(1), complex128(1), datetime64[ns](1), float64(1), int16(1), int32(1), int64(1), int8(1), object(1), timedelta64[ns](1), uint8(1)
memory usage: 463.8 KB
memory_usage
每列的内存使用情况可以通过调用memory_usage()方法得到。 这将返回一个 Series，其索引由列名和每列的内存使用情况表示，以字节为单位。 对于上面的DataFrame，可以通过memory_usage方法查看每一列的内存使用量和总内存使用量：
如果要获取准确内存时候情况，可以开启参数deep=True
df.memory_usage(deep=True)
output
Index 128
col_int8 5000
col_uint8 5000
col_int16 10000
col_int32 20000
col_int64 40000
col_float64 40000
col_datetime64[ns] 40000
col_timedelta64[ns] 40000
col_complex128 80000
col_object 179800
col_bool 5000
categorical 9968
dtype: int64
df.memory_usage(deep=True).sum()
#output
474896
Data typeDescription
Boolean (True or False) stored as a byte
Default integer type (same as C
; normally either
or
Identical to C
(normally
or
Integer used for indexing (same as C
; normally either
or
Byte (-128 to 127)
Integer (-32768 to 32767)
Integer (-2147483648 to 2147483647)
Integer (-9223372036854775808 to 9223372036854775807)
Unsigned integer (0 to 255)
Unsigned integer (0 to 65535)
Unsigned integer (0 to 4294967295)
Unsigned integer (0 to 18446744073709551615)
Shorthand for
Half precision float: sign bit, 5 bits exponent, 10 bits mantissa
Single precision float: sign bit, 8 bits exponent, 23 bits mantissa
Double precision float: sign bit, 11 bits exponent, 52 bits mantissa
Shorthand for
Complex number, represented by two 32-bit floats
Complex number, represented by two 64-bit floats
Original: https://blog.csdn.net/haohaizijhz/article/details/122722847
Author: 只要开始永远不晚
Title: pandas的内存使用
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