PANDAS MOST USED

Create a dataframe from a dict

mydict = {'Name': 'Jax', 'Age': 'Teller', 'City': 'Antioch'}
df  = pd.DataFrame(mydict, index=[0]) # 0th row
df2 = pd.DataFrame.from_dict(mydict, orient='index') # 0th column

Make Sure It Doesn’t Contain NaN or Empty

print("Does it contain NaN: ", df["COLUMN"].isna().values.any() or df.empty)
# Rows that contain nan values
print(df[df.isna().any(axis=1)])

Drop rows with NaN values in the required columns

df = df.dropna(subset=required_columns)  

Check something if it’s in columns

if column not in train.columns:
	print(f"{column} is not in columns")

Convert to datetime format

df['date'] = pd.to_datetime(df['date'])
# check if pandas.to_datetime is working
print("Datetime: ", df['date'].dtype)

Drop columns except wanted ones

df = df[['FECHA_MEDIDA', 'MEDIDA_numeric']]

Drop columns

df = df.drop(columns=['low_fats', 'recyclable'])

Get row info from a df

df[(df["PERFIL"] == "Suleyman") & (df["PROYECTO"] == "OTRAS ACTIVIDADES")]

Ensure column names are stripped of spaces

df.columns = df.columns.str.strip()        

Strip and lower the column names