Machine Learning with Python on Telegram: “import pandas as pd df = pd.DataFrame({'Fruit':…”

import pandas as pd
df = pd.DataFrame({'Fruit': ['Apple', 'Banana', 'Apple', 'Orange']})
print(df['Fruit'].nunique())

#12. df.isnull()
Returns a DataFrame of boolean values indicating missing values.

import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, np.nan], 'B': [np.nan, 'x']})
print(df.isnull())

A      B
0  False   True
1   True  False

#13. df.isnull().sum()
Returns the number of missing values in each column.

import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, np.nan, 3, np.nan], 'B': [5, 6, 7, 8]})
print(df.isnull().sum())

A    2
B    0
dtype: int64

#14. df.to_csv()
Writes the DataFrame to a comma-separated values (csv) file.

import pandas as pd
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
csv_output = df.to_csv(index=False)
print(csv_output)

A,B
1,3
2,4

#15. df.copy()
Creates a deep copy of a DataFrame.

import pandas as pd
df1 = pd.DataFrame({'A': [1]})
df2 = df1.copy()
df2.loc[0, 'A'] = 99
print(f"Original df1:\n{df1}")
print(f"Copied df2:\n{df2}")

Original df1:
   A
0  1
Copied df2:
    A
0  99

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#DataAnalysis #Pandas #Selection #Indexing

Part 2: Pandas - Data Selection & Indexing

#16. df['col']
Selects a single column as a Series.

import pandas as pd
df = pd.DataFrame({'Name': ['Alice', 'Bob'], 'Age': [30, 25]})
print(df['Name'])

0    Alice
1      Bob
Name: Name, dtype: object

#17. df[['col1', 'col2']]
Selects multiple columns as a new DataFrame.

import pandas as pd
df = pd.DataFrame({'Name': ['Alice'], 'Age': [30], 'City': ['New York']})
print(df[['Name', 'City']])

Name       City
0  Alice   New York

#18. df.loc[]
Accesses a group of rows and columns by label(s) or a boolean array.

import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3]}, index=['x', 'y', 'z'])
print(df.loc['y'])

A    2
Name: y, dtype: int64

#19. df.iloc[]
Accesses a group of rows and columns by integer position(s).

import pandas as pd
df = pd.DataFrame({'A': [10, 20, 30]})
print(df.iloc[1])

A    20
Name: 1, dtype: int64

#20. df[df['col'] > value]
Selects rows based on a boolean condition (boolean indexing).

import pandas as pd
df = pd.DataFrame({'Age': [22, 35, 18, 40]})
print(df[df['Age'] > 30])

Age
1   35
3   40

#21. df.set_index()
Sets the DataFrame index using existing columns.

import pandas as pd
df = pd.DataFrame({'Country': ['USA', 'UK'], 'Code': [1, 44]})
df_indexed = df.set_index('Country')
print(df_indexed)

Code
Country      
USA         1
UK         44

#22. df.reset_index()
Resets the index of the DataFrame and uses the default integer index.

import pandas as pd
df = pd.DataFrame({'Code': [1, 44]}, index=['USA', 'UK'])
df_reset = df.reset_index()
print(df_reset)

index  Code
0   USA     1
1    UK    44

#23. df.at[]
Accesses a single value by row/column label pair. Faster than .loc.