---
description: Other useful functions, notes, class
---

# Useful functions

```python
import numpy as np
import pandas as pd
import glob
import shutil
import os
import sqlite3
from copy import deepcopy
from datetime import datetime,timedelta,date
```

### About dataframe

```python
# Get memory used 
def get_mem_usage(df): 
    print(f"{df.memory_usage(deep=True).sum()/1024 **2:3.2f}Mb")


# Get basic info
def get_general_info(df):
    name = [x for x in globals() if globals()[x] is df][0]
    print(f"Dataframe << {name}>>has {df.shape[0]} rows, {df.shape[1]} columns")
    print("=======================================")
    print("Column Types:\n")
    print(df.dtypes)
    print("=======================================")
    print("Missing values per column: ")
    percent_missing = df.isnull().sum()*100 / len(df)
    missing_value_df = pd.DataFrame({'column_name':df.columns,'percent_missing':percent_missing})
    missing_value_df['percent_missing'] = ["{:.2f}%".format(x) for x in missing_value_df['percent_missing'] ]
    print(missing_value_df)
    print("=======================================")
    print(f"Memory Use: {df.memory_usage(deep=True).sum()/1024 **2:3.2f}Mb")    
    print("=======================================")
    print("Missing Values in columns: ")
    print(df.isnull().sum())
    
# Change col format
def change_col_format(df,target_type):
    for c in df.columns:
        df[c] = df[c].astype(target_type)
    return df
    
# Optimize a df
def get_optimize_df(df):
    df.astype({col:'category' for col in df.columns if df[col].nunique() / df[col].shape[0]<0.5})
    return df
    

# Save / read pickle
def save_as_pickle(df,name,path=None):
    try:
        if path==None:
            df.to_pickle(f"{name}.pkl")
            print(f"Dataframe saved as pickle in => {os.getcwd()}")
        else:
            current_path = os.getcwd()
            df.to_pickle(f"{path}/{name}.pkl")
            print(f"Dataframe saved as pickle in => {path}/{name}.pkl")
            os.chdir(current_path)
    except:
        print("Save failed. Make sure it's a dataframe or the path is correct")

def read_pickle_as_df(path=None):
    result={}
    current_path = os.getcwd()
    target_path = os.getcwd()
    if path!=None:
        target_path = path

    os.chdir(target_path)
    lst = glob.glob(f"*.pkl")
        
    for p in lst:
        name = p.split(".")[0]
        result[name]=pd.read_pickle(p)
    
    os.chdir(current_path)
    return result

# Read big csv file    
def read_large_csv(name,chunkSize=1000000,encoding='utf-8'):

    reader = pd.read_csv(name,iterator=True,encoding=encoding)
    chunks=[]
    loop=True
    while loop:
        try:
            chunk = reader.get_chunk(chunkSize)
            chunks.append(chunk)
        except StopIteration:
            loop=False
    df = pd.concat(chunks,ignore_index=True)
    return df

```

### Files

```python
# Return a list of file with assigned extension
def get_files(extension):
    return glob.glob(f"*.{extension}")

# Remove the folder if exist and create new one with same name 
def create_clean_dir(name):
    if os.path.isdir(name):
        shutil.rmtree(name)
        os.makedirs(name)
    else:
        os.makedirs(name)
    os.chdir(name)
    print(f"Current working dir => :{os.getcwd()}")
    
    
# Create a temporary dir
import tempfile
from contextlib import contextmanager


@contextmanager
def tmpdir(path=None):
    if path:
        tmpdir = tempfile.TemporaryDirectory(dir=path)
    else:
        tmpdir = tempfile.TemporaryDirectory()
    try:
        yield tmpdir
        print(tmpdir.name)
    finally:
        try:
            tmpdir.cleanup()
        except OSError as e:
            print(e)
                
default_path="F:\\"

with tmpdir(default_path) as tmp:
    # Do sth
    time.sleep(5)
```

### Time

```python
def get_n_days_ago(n=0,time_format="%d-%m-%Y"):
    time_stamp = datetime.now()-timedelta(days=n)
    return time_stamp.strftime(time_format)
    
 
# strftime("%d/%m/%Y, %H:%M:%S")  
# 05/13/2019, 13:38:14
```

### Test iterable

```python
def is_iterable(obj):
    try:
        iter(obj)
        return True
    except TypeError:
        return False
        
        
obj = 100
if is_iterable(obj):
    for i in obj:
        print(i)
else:
    print('Error: obj is not iterable')        

Error: obj is not iterable
```

### Get df from list of tuples

```python
df = pd.DataFrame.from_records(
   [namedtuple_instance1, namedtuple_instance2],
   columns=namedtuple_type._fields
)
```

### Split a list to groups with almost equal file size

```python
def makeGroup(lst:List,n:int)->List:
    lists = [[] for _ in range(n)]
    totals = [(0, i) for i in range(n)]
    heapq.heapify(totals)
    for value in lst:
        total, index = heapq.heappop(totals)
        lists[index].append(value)
        heapq.heappush(totals, (total + value, index))
    return lists
```

### Add extra params in command

```python
import os
from glob import glob
import heapq

def filterOutEmptyFiles(files):
    return [ f for f in files if os.stat(f).st_size!=0]

# Split a list to different groups
def makeGroup(lst, n):
    lists = [[] for _ in range(n)]
    totals = [(0, i) for i in range(n)]
    heapq.heapify(totals)
    for value in lst:
        total, index = heapq.heappop(totals)
        lists[index].append(value)
        heapq.heappush(totals, (total + value, index))
    return lists
    

def groupFiles(pqFiles,numberGroup):
    DownloadParquet = namedtuple("DownloadParquet", "name size")
    allDownload = [DownloadParquet(name = f,size=os.stat(f).st_size) for f in pqFiles]
    sizeList = [d.size for d in allDownload ]
    afterSizeGroupe= makeGroup(sizeList,numberGroup)
    result = []   
    for each in afterSizeGroupe:
        tmp=[]
        for everyFileSize in each:
            for d in allDownload:
                if d.size==everyFileSize:
                    tmp.append(d.name)
        result.append(tmp)
    del allDownload,sizeList,afterSizeGroupe
    return result
    
    
```

```python
import os, sys, ast
import pandas as pd

def main():
    
    START_DATE = sys.argv[1] 
    END_DATE = sys.argv[2]
    ORIGIN_BUCKETS = ast.literal_eval(sys.argv[3]) 
    DESTINATION_BUCKET = sys.argv[4]
    DATA_TYPE = sys.argv[5]
    FLATTEN = sys.argv[6]

    dtRange = pd.date_range(start=START_DATE, end=END_DATE)
    for dt in dtRange:
        print(dt.strftime('%Y%m%d'))


    print(START_DATE)
    print(END_DATE)
    print(ORIGIN_BUCKETS)
    print(DESTINATION_BUCKET)
    print(DATA_TYPE)
    print(FLATTEN)

    print('a'==ORIGIN_BUCKETS[0])
    print('ttevent'==DATA_TYPE)

if __name__ == '__main__':
    main()

# In cmd
python test.py 2021-09-01 2021-09-03 "['a','b']"  dest ttevent true

20210901
20210902
20210903
2021-09-01
2021-09-03
['a', 'b']
dest
ttevent
true
True
True
```

### &#x20;Filter out empty files

```python
def filterOutEmptyFiles(files):
    return [ f for f in files if os.stat(f).st_size!=0]
```

### Convert a file size to mb,gb...

```python
def convertSize(sizeBytes:int)->Union[int,str]:
    if sizeBytes == 0:
        return "0B"
    size_name = ("B", "KB", "MB", "GB", "TB", "PB", "EB", "ZB", "YB")
    i = int(math.floor(math.log(sizeBytes, 1024)))
    p = math.pow(1024, i)
    s = round(sizeBytes / p, 2)
    return (sizeBytes, f"{s} {size_name[i]}")

```
