How to optimize the code and reduce memory usage Python

Question

The purpose is to reduce memory usage . Meaning that it should be optimized in a way that the hash is equal to the test hash .

What I've tried so far:

1. Adding __slots__ but it didn't make any changes.
2. Change default dtype float64 to float32 . Although it reduces the mem usage significantly, it brakes the test by changing the hash.
3. Converted data into np.array reduced CPU times: from 13 s to 2.05 s but didn't affect the memory usage .

The code to reproduce:

rows = 40000000
trs = 10

random.seed(42)

generated_data: tp.List[float] = np.array([random.random() for _ in range(rows)])



def df_upd(df_initial: pd.DataFrame, df_new: pd.DataFrame) -> pd.DataFrame:
    return pd.concat((df_initial, df_new), axis=1)


class T:
    """adding a column of random data"""
    __slots__ = ['var']
    def __init__(self, var: float):
        self.var = var

    def transform(self, df_initial: pd.DataFrame) -> pd.DataFrame:
        return df_upd(df_initial, pd.DataFrame({self.var: generated_data}))


class Pipeline:
    __slots__ = ['df', 'transforms']
    def __init__(self):
        self.df = pd.DataFrame()
        self.transforms = np.array([T(f"v{i}") for i in range(trs)])

    def run(self):
        for t in self.transforms:
            self.df = t.transform(self.df)
        return self.df


if __name__ == "__main__":
    
    
    # starting the monitoring
    tracemalloc.start()

    # function call
    pipe = Pipeline()
    %time df = pipe.run()
    print("running")

    # displaying the memory
    current, peak = tracemalloc.get_traced_memory()
    print(f"Current memory usage is {current / 10**3} KB ({(current / 10**3)*0.001} MB); Peak was {peak / 10**3} KB ({(peak / 10**3)*0.001} MB); Diff = {(peak - current) / 10**3} KB ({((peak - current) / 10**3)*0.001} MB)")

    # stopping the library
    tracemalloc.stop()
    
    # should stay unchanged
    %time hashed_df = hashlib.sha256(pd.util.hash_pandas_object(df, index=True).values).hexdigest()
    print("hashed_df", hashed_df)    
    
    assert hashed_df == test_hash

    print("Success!")

Answer 1

If you avoid pd.concat() and use the preferred way of augmenting dataframes:

df["new_col_name"] = new_col_data

this will reduce peak memory consumption significantly.

---

In your code it is sufficient to fix the Transform class:

class Transform:
    """adding a column of random data"""
    __slots__ = ['var']
    def __init__(self, var: str):
        self.var = var

    def transform(self, df: pd.DataFrame) -> pd.DataFrame:
        df[self.var] = generated_data
        return df

(Note that I also changed the type of var from float to str to reflect how it is used in the code).

---

In my machine I went from:

Current memory usage is 1600110.987 KB (1600.110987 MB); Peak was 4480116.325 KB (4480.116325 MB); Diff = 2880005.338 KB (2880.005338 MB)

to:

Current memory usage is 1760101.105 KB (1760.101105 MB); Peak was 1760103.477 KB (1760.1034769999999 MB); Diff = 2.372 KB (0.002372 MB)

(I am not sure why the current memory usage is slightly higher in this case).

---

For faster computation, you may want to do some pre-allocation.

To do that, you could replace, in Pipeline's __init__() :

self.df = pd.DataFrame()

with:

self.df = pd.DataFrame(data=np.empty((rows, trs)), columns=[f"v{i}" for i in range(trs)])

---

If you want to get even faster, you can compute the DataFrame right away in the Pipeline's __init__ , eg:

class Pipeline:
    __slots__ = ['df', 'transforms']
    def __init__(self):
        self.df = pd.DataFrame(data=generated_data[:, None] + np.zeros(trs)[None, :], columns=[f"v{i}" for i in range(trs)])

    def run(self):
        return self.df

but I assume your Transform is a proxy of a more complex operation and I am not sure this simplification is easy to adapt beyond the toy code in the question.