A Python package to reduce the memory usage of pandas DataFrames. It speeds up your workflow and reduces the risk of running out of memory.
Project description
Less memory usage - more speed
A Python package to reduce the memory usage of pandas DataFrames without changing the underlying data. It speeds up your workflow and reduces the risk of running out of memory.
Installation
pip install a-pandas-ex-less-memory-more-speed
Usage
df = pd.read_csv( "https://github.com/pandas-dev/pandas/raw/main/doc/data/titanic.csv",)
from random import choice
#Let's add some more data types
truefalse = lambda: choice([True, False])
df['truefalse'] = [truefalse() for x in range(len(df))]
df['onlynan'] = pd.NA
df['nestedlists'] = [[[1]*10]] * len(df)
mixedstuff = lambda: choice([True, False, 'right', 'wrong', 1,2,23,343.555,23.444, [442,553,44], [],''])
df['mixedstuff'] =[mixedstuff() for x in range(len(df))]
floatnumbers = lambda: choice([33.44,344.42424265,15.0,3222.33])
df['floatnumbers']=[floatnumbers() for x in range(len(df))]
floatnumbers0 = lambda: choice([33.0,344.0,15.0,3222.0])
df['floatnumbers0']=[floatnumbers0() for x in range(len(df))]
intwithnan = lambda: choice([1,2,3,4,5,pd.NA])
df['intwithnan']=[intwithnan() for x in range(len(df))]
df2 = optimize_dtypes(
dframe=df,
point_zero_to_int=True,
categorylimit=15,
verbose=True,
include_na_strings_in_pd_na=True,
include_empty_iters_in_pd_na=True,
include_0_len_string_in_pd_na=True,
convert_float=True,
check_float_difference=True,
float_tolerance_negative=-0.1,
float_tolerance_positive=0.1,
)
print(df)
print(df2)
print(df.dtypes)
print(df2.dtypes)
Memory usage of dataframe is: 0.12333202362060547 MB
█████████████████████████████
Analyzing df.PassengerId
----------------
df.PassengerId Is numeric!
df.PassengerId Max: 891
df.PassengerId Min: 1
df.PassengerId: Only .000 in columns -> Using int - Checking which size fits best ...
df.PassengerId: Using dtype: np.uint16
█████████████████████████████
Analyzing df.Survived
----------------
df.Survived Is numeric!
df.Survived Max: 1
df.Survived Min: 0
df.Survived: Only .000 in columns -> Using int - Checking which size fits best ...
df.Survived: Using dtype: np.uint8
█████████████████████████████
Analyzing df.Pclass
----------------
df.Pclass Is numeric!
df.Pclass Max: 3
df.Pclass Min: 1
df.Pclass: Only .000 in columns -> Using int - Checking which size fits best ...
df.Pclass: Using dtype: np.uint8
█████████████████████████████
Analyzing df.Name
----------------
df.Name: Using dtype: string
█████████████████████████████
Analyzing df.Sex
----------------
df.Sex: Using dtype: category
█████████████████████████████
Analyzing df.Age
----------------
df.Age Is numeric!
df.Age Max: 80.0
df.Age Min: 0.42
df.Age: Using dtype: Float64
█████████████████████████████
Analyzing df.SibSp
----------------
df.SibSp Is numeric!
df.SibSp Max: 8
df.SibSp Min: 0
df.SibSp: Only .000 in columns -> Using int - Checking which size fits best ...
df.SibSp: Using dtype: np.uint8
█████████████████████████████
Analyzing df.Parch
----------------
df.Parch Is numeric!
df.Parch Max: 6
df.Parch Min: 0
df.Parch: Only .000 in columns -> Using int - Checking which size fits best ...
df.Parch: Using dtype: np.uint8
█████████████████████████████
Analyzing df.Ticket
----------------
df.Ticket: Using dtype: string
█████████████████████████████
Analyzing df.Fare
----------------
df.Fare Is numeric!
df.Fare Max: 512.3292
df.Fare Min: 0.0
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
Max. positive difference - limit 0.1
498 -0.05
305 -0.05
708 -0.05
Max. negative difference - limit -0.1
679 0.1708
258 0.1708
737 0.1708
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
------------- <class 'numpy.float16'> ------------- not right for df.Fare
Checking next dtype...
True -> within the desired range: 0.1 / -0.1
False 5
True 886
-------------------
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
Max. positive difference - limit 0.1
0 0.0
587 0.0
588 0.0
Max. negative difference - limit -0.1
0 0.0
598 0.0
587 0.0
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
+++++++++++++ <class 'numpy.float32'> +++++++++++++ right for df.Fare
True -> within the desired range: 0.1 / -0.1
True 891
-------------------
df.Fare: Using dtype: np.float32
█████████████████████████████
Analyzing df.Cabin
----------------
df.Cabin: Using dtype: string
█████████████████████████████
Analyzing df.Embarked
----------------
df.Embarked: Using dtype: category
█████████████████████████████
Analyzing df.truefalse
----------------
df.truefalse: Using dtype: np.bool_
█████████████████████████████
Analyzing df.onlynan
----------------
df.onlynan Is numeric!
df.onlynan Max: nan
df.onlynan Min: nan
df.onlynan: Only nan in column, continue ...
█████████████████████████████
Analyzing df.nestedlists
----------------
█████████████████████████████
Analyzing df.mixedstuff
----------------
█████████████████████████████
Analyzing df.floatnumbers
----------------
df.floatnumbers Is numeric!
df.floatnumbers Max: 3222.33
df.floatnumbers Min: 15.0
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
Max. positive difference - limit 0.1
890 -0.33
597 -0.33
592 -0.33
Max. negative difference - limit -0.1
527 0.075757
190 0.075757
171 0.075757
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
------------- <class 'numpy.float16'> ------------- not right for df.floatnumbers
Checking next dtype...
True -> within the desired range: 0.1 / -0.1
False 219
True 672
-------------------
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
Max. positive difference - limit 0.1
0 0.0
587 0.0
588 0.0
Max. negative difference - limit -0.1
0 0.0
598 0.0
587 0.0
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
+++++++++++++ <class 'numpy.float32'> +++++++++++++ right for df.floatnumbers
True -> within the desired range: 0.1 / -0.1
True 891
-------------------
df.floatnumbers: Using dtype: np.float32
█████████████████████████████
Analyzing df.floatnumbers0
----------------
df.floatnumbers0 Is numeric!
df.floatnumbers0 Max: 3222.0
df.floatnumbers0 Min: 15.0
df.floatnumbers0: Only .000 in columns -> Using int - Checking which size fits best ...
df.floatnumbers0: Using dtype: np.uint16
█████████████████████████████
Analyzing df.intwithnan
----------------
df.intwithnan Is numeric!
df.intwithnan Max: 5
df.intwithnan Min: 1
df.intwithnan: Only .000 in columns -> Using int - Checking which size fits best ...
df.intwithnan: Using dtype: Int64
█████████████████████████████
Memory usage of dataframe was: 0.12333202362060547 MB
Memory usage of dataframe is now: 0.07259273529052734 MB
This is 58.85959960718511 % of the initial size
█████████████████████████████
█████████████████████████████
PassengerId Survived Pclass ... floatnumbers floatnumbers0 intwithnan
0 1 0 3 ... 33.440000 33.0 4
1 2 1 1 ... 3222.330000 15.0 5
2 3 1 3 ... 33.440000 33.0 3
3 4 1 1 ... 15.000000 33.0 1
4 5 0 3 ... 15.000000 344.0 2
.. ... ... ... ... ... ... ...
886 887 0 2 ... 344.424243 344.0 5
887 888 1 1 ... 15.000000 15.0 4
888 889 0 3 ... 344.424243 3222.0 2
889 890 1 1 ... 344.424243 3222.0 4
890 891 0 3 ... 3222.330000 3222.0 <NA>
[891 rows x 19 columns]
PassengerId Survived Pclass ... floatnumbers floatnumbers0 intwithnan
0 1 0 3 ... 33.439999 33 4
1 2 1 1 ... 3222.330078 15 5
2 3 1 3 ... 33.439999 33 3
3 4 1 1 ... 15.000000 33 1
4 5 0 3 ... 15.000000 344 2
.. ... ... ... ... ... ... ...
886 887 0 2 ... 344.424255 344 5
887 888 1 1 ... 15.000000 15 4
888 889 0 3 ... 344.424255 3222 2
889 890 1 1 ... 344.424255 3222 4
890 891 0 3 ... 3222.330078 3222 <NA>
[891 rows x 19 columns]
PassengerId int64
Survived int64
Pclass int64
Name object
Sex object
Age float64
SibSp int64
Parch int64
Ticket object
Fare float64
Cabin object
Embarked object
truefalse bool
onlynan object
nestedlists object
mixedstuff object
floatnumbers float64
floatnumbers0 float64
intwithnan object
dtype: object
PassengerId uint16
Survived uint8
Pclass uint8
Name string
Sex category
Age Float64
SibSp uint8
Parch uint8
Ticket string
Fare float32
Cabin string
Embarked category
truefalse bool
onlynan object
nestedlists object
mixedstuff object
floatnumbers float32
floatnumbers0 uint16
intwithnan Int64
dtype: object
Parameters:
dframe: Union[pd.Series, pd.DataFrame]
pd.Series, pd.DataFrame
point_zero_to_int: bool
Convert float to int if all float numbers in the column end with .0+
(default = True)
categorylimit: int
Convert strings to category, when ratio len(df) / len(df.value_counts) >= categorylimit
(default = 4)
verbose: bool
Keep track of what is happening
(default = True)
include_na_strings_in_pd_na: bool
When True -> treated as nan:
[
"<NA>",
"<NAN>",
"<nan>",
"np.nan",
"NoneType",
"None",
"-1.#IND",
"1.#QNAN",
"1.#IND",
"-1.#QNAN",
"#N/A N/A",
"#N/A",
"N/A",
"n/a",
"NA",
"#NA",
"NULL",
"null",
"NaN",
"-NaN",
"nan",
"-nan",
]
(default =True)
include_empty_iters_in_pd_na: bool
When True -> [], {} are treated as nan (default = False )
include_0_len_string_in_pd_na: bool
When True -> '' is treated as nan (default = False )
convert_float: bool
Don't convert columns containing float numbers.
Comparing the 2 dataframes from the example, one can see that float numbers frequently
don't have the exact same value as the original float number.
If decimal digits are important for your work, disable it!
(default=True)
check_float_difference: bool
If a little difference between float dtypes is fine for you, use True
Ignored if convert_float=False
(default=True)
float_tolerance_negative: float
The negative tolerance you can live with, e.g.
3222.330078 - 3222.330000 = 0.000078 is fine for you
Ignored if convert_float=False
(default= -0.05)
float_tolerance_positive: float = 0.05,
The positive tolerance you can live with
3222.340078 - 3222.330000 = 0.010078 is fine for you
Ignored if convert_float=False
(default= 0.05)
Returns:
Union[pd.DataFrame, pd.Series]
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