Creating a DataFrame from Python Objects

A Pandas DataFrame consists of multiple Pandas Series that share index values.

np.random.seed(101)
mydata = np.random.randint(0,101,(4,3))
myindex = ['CA','NY','AZ','TX']
mycolumns = ['Jan','Feb','Mar']
df = pd.DataFrame(data=mydata,index=myindex,columns=mycolumns)
 
df.info()

Obtaining Basic Information About DataFrame

df.columns
df.index
df.head(3)
df.tail(3)
df.describe() #Statistical summary
df.describe().transpose()

Column Operations

Selection:

df['total_bill']          # Single column → Series
df[['total_bill','tip']]  # Multiple columns → DataFrame

Modification:

# Create new column
df['tip_pct'] = 100 * df['tip'] / df['total_bill']
 
# Round values
df['price_per_person'] = np.round(df['total_bill']/df['size'], 2)
 
# Delete column
df = df.drop('tip_pct', axis=1) 

Index Management:

df.set_index('Payment ID', inplace=True)  # Set custom index
df.reset_index(inplace=True)              # Revert to default

Row Operations

Selection:

df.iloc[0]        # By position → Series
df.loc['Sun2959'] # By index label → Series
 
df.iloc[0:4]              # Slice by position
df.loc[['Sun2959','Sun5260']]  # Specific labels

Modification:

# Delete row
df.drop('Sun2959', axis=0)
 
# Add row (rarely used)
new_row = df.iloc[0]
df.append(new_row)

Pro Tips

1. Path Check: Use pwd and ls to verify file locations

2. Axis Remember:

   • axis=0 → Rows (vertical)

   • axis=1 → Columns (horizontal)

3. Modification Best Practices:

   • Use inplace=True or reassign (df = df.drop(...))

   • Prefer vectorized operations over loops

• These changes are not permanent, unless like df=df.drop('Sun2959', axis=0)
• Sometimes, selecting logic is easier than dropping logic, so maybe we can use selecting logic more.