DataFrame useful

import pandas as pd

# Create a sample DataFrame
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'],
    'Age': [25, 30, 35, 40, 45],
    'City': ['New York', 'Los Angeles', 'New York', 'New York', 'Los Angeles']
}

df = pd.DataFrame(data)

age_threshold = 30
result = df.query('Age > @age_threshold and City == "New York"')
print(result)
#       Name  Age      City
# 2  Charlie   35  New York
# 3    David   40  New York

query('`First Name` == "Alice"')

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [7, 8, 9]
})

# Rename columns 'A' to 'Alpha' and 'B' to 'Beta'
# If `inplace=Ture`, modify original df
df.rename(columns={'A': 'Alpha', 'B': 'Beta'}, inplace=True)

print(df)
#    Alpha  Beta  C
# 0      1     4  7
# 1      2     5  8
# 2      3     6  9

# Assign new column names
df.columns = ['Alpha', 'Beta', 'Gamma']

print(df)
#    Alpha  Beta  C
# 0      1     4  7
# 1      2     5  8
# 2      3     6  9

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [7, 8, 9]
})

# Take care with type
replace_dict = {'1':'4', '2':'5', '3':'6'}
df['A'] = df['A'].astype(str).replace(replace_dict)
print(df)
#    A  B  C
# 0  4  4  7
# 1  5  5  8
# 2  6  6  9