data processing tips

https://towardsdatascience.com/why-and-how-to-use-pandas-with-large-data-9594dda2ea4c

1. Read CSV file data in chunk size
# read the large csv file with specified chunksize 
df_chunk = pd.read_csv(r'../input/data.csv', chunksize=1000000)

chunk_list = []  # append each chunk df here 

# Each chunk is in df format
for chunk in df_chunk:  
    # perform data filtering 
    chunk_filter = chunk_preprocessing(chunk)
    
    # Once the data filtering is done, append the chunk to list
    chunk_list.append(chunk_filter)
    
# concat the list into dataframe 
df_concat = pd.concat(chunk_list)


2. Filter out unimportant columns to save memory
# Filter out unimportant columns
df = df[['col_1','col_2', 'col_3', 'col_4', 'col_5', 'col_6','col_7', 'col_8', 'col_9', 'col_10']]

3. Change dtypes for columns
# Change the dtypes (int64 -> int32)
df[['col_1','col_2', 
    'col_3', 'col_4', 'col_5']] = df[['col_1','col_2', 
                                      'col_3', 'col_4', 'col_5']].astype('int32')

# Change the dtypes (float64 -> float32)
df[['col_6', 'col_7',
    'col_8', 'col_9', 'col_10']] = df[['col_6', 'col_7',
                                       'col_8', 'col_9', 'col_10']].astype('float32')
                                       
https://towardsdatascience.com/the-simple-yet-practical-data-cleaning-codes-ad27c4ce0a38

1. Drop multiple columns
def drop_multiple_col(col_names_list, df): 
    '''
    AIM    -> Drop multiple columns based on their column names 
    
    INPUT  -> List of column names, df
    
    OUTPUT -> updated df with dropped columns 
    ------
    '''
    df.drop(col_names_list, axis=1, inplace=True)
    return df
    
2.Change dtypes
def change_dtypes(col_int, col_float, df): 
    '''
    AIM    -> Changing dtypes to save memory
     
    INPUT  -> List of column names (int, float), df
    
    OUTPUT -> updated df with smaller memory  
    ------
    '''
    df[col_int] = df[col_int].astype('int32')
    df[col_float] = df[col_float].astype('float32')
    
 3. Convert categorical variable to numerical variable
 def convert_cat2num(df):
    # Convert categorical variable to numerical variable
    num_encode = {'col_1' : {'YES':1, 'NO':0},
                  'col_2'  : {'WON':1, 'LOSE':0, 'DRAW':0}}  
    df.replace(num_encode, inplace=True)  
    
 4. Check missing data  -- Which columns have higher number of missing data 
 def check_missing_data(df):
    # check for any missing data in the df (display in descending order)
    return df.isnull().sum().sort_values(ascending=False)
 
 5. Remove strings in columns
 
def remove_col_str(df):
    # remove a portion of string in a dataframe column - col_1
    df['col_1'].replace('\n', '', regex=True, inplace=True)

    # remove all the characters after &# (including &#) for column - col_1
    df['col_1'].replace(' &#.*', '', regex=True, inplace=True)

6. Remove white space in columns
def remove_col_white_space(df,col):
    # remove white space at the beginning of string 
    df[col] = df[col].str.lstrip()
    
7. Concatenate two columns with strings (with condition)
def concat_col_str_condition(df):
    # concat 2 columns with strings if the last 3 letters of the first column are 'pil'
    mask = df['col_1'].str.endswith('pil', na=False)
    col_new = df[mask]['col_1'] + df[mask]['col_2']
    col_new.replace('pil', ' ', regex=True, inplace=True)  # replace the 'pil' with emtpy space

8. Convert timestamp(from string to datetime format)
def convert_str_datetime(df): 
    '''
    AIM    -> Convert datetime(String) to datetime(format we want)
     
    INPUT  -> df
    
    OUTPUT -> updated df with new datetime format 
    ------
    '''
    df.insert(loc=2, column='timestamp', value=pd.to_datetime(df.transdate, format='%Y-%m-%d %H:%M:%S.%f'))