Pandas is a Python library used for data manipulation and analysis. It provides powerful tools to work with structured data like tables, similar to Excel but more powerful. Imagine you have a large spreadsheet with thousands of rows. Pandas allows you to filter, sort, and analyze this data quickly and efficiently.

A DataFrame is a 2-dimensional table with rows and columns, like an Excel sheet. Each column can hold different types of data (integers, strings, etc.).

import pandas as pd

data = {'Name': ['Alice', 'Bob'], 'Score': [90, 85]}
df = pd.DataFrame(data)
print(df)

You can create a DataFrame by passing a dictionary, list, or other data structures to Pandas.

import pandas as pd

data = {'Name': ['Alice', 'Bob'], 'Math': [90, 80]}
df = pd.DataFrame(data)
print(df)

A Series is a one-dimensional array-like structure with labels, which can hold any data type (integers, strings, etc.). It's like a single column in a DataFrame.

import pandas as pd

scores = pd.Series([90, 85, 78])
print(scores)

You can select a column from a DataFrame by using the column name in square brackets.

df['Math']

You can add a new column by assigning a list or Series to a new column name.

df['Science'] = [85, 78]
print(df)

You can remove a column using the drop() method.

df.drop('Science', axis=1, inplace=True)
print(df)

You can filter rows by applying conditions on columns.

df[df['Math'] > 80]

loc is used to access rows and columns by labels, while iloc is used to access rows and columns by integer position.

df.loc[0]
df.iloc[0]

You can check for missing values using the isnull() method, which returns a DataFrame of the same shape with True where values are missing and False elsewhere. Example:

df.isnull()

You can fill missing values using the fillna() method.

df['Math'].fillna(df['Math'].mean(), inplace=True)
print(df)

You can drop rows with missing values using the dropna() method.

df.dropna(inplace=True)
print(df)

apply() is used to apply a function along an axis of the DataFrame, while map() is used to apply a function element-wise on a Series.

def add_ten(x):
    return x + 10

df['Math'] = df['Math'].apply(add_ten)
print(df)

You can merge two DataFrames using the merge() method, similar to SQL joins.

pd.merge(df1, df2, on='StudentID')

groupby() is used to split the data into groups based on some criteria, apply a function to each group, and combine the results.

df.groupby('Class').mean()

You can concatenate two DataFrames using pd.concat().

df = pd.concat([df1, df2])
print(df)

describe() provides a summary of statistics for numerical columns, including count, mean, std, min, and percentiles.

df.describe()

You can sort a DataFrame by a specific column using the sort_values() method.

df.sort_values('Math', inplace=True)
print(df)

You can convert a DataFrame to a NumPy array using the to_numpy() method.

arr = df.to_numpy()
print(arr)

df.any() checks if any element in the DataFrame is True (or non-zero), while df.all() checks if all elements are True.

df['Math'].any()
df['Math'].all()

You can reset the index of a DataFrame using the reset_index() method.

df.reset_index(drop=True, inplace=True)
print(df)

You can rename columns using the rename() method.

df.rename(columns={'Math': 'Mathematics'}, inplace=True)
print(df)

You can drop duplicates using the drop_duplicates() method.

df.drop_duplicates(inplace=True)
print(df)

pivot_table() is used to create a pivot table, which is a table that summarizes data.

pd.pivot_table(df, values='Sales', index=['Product'], columns=['Region'], aggfunc=np.sum)

You can set a column as the index using the set_index() method.

df.set_index('StudentID', inplace=True)
print(df)

cut() is used to bin continuous data into discrete intervals.

df['Score_Category'] = pd.cut(df['Score'], bins=[0, 60, 80, 100], labels=['Low', 'Medium', 'High'])
print(df)

You can calculate the rolling mean using the rolling() method followed by mean().

df['Math_Rolling_Mean'] = df['Math'].rolling(3).mean()
print(df)

You can create a copy of a DataFrame using the copy() method. This is useful when you want to make changes to a DataFrame without affecting the original one. Example:

df_copy = df.copy()
print(df_copy)

melt() is used to transform or reshape data, turning columns into rows. It's often used when you need to unpivot a DataFrame, making it longer and narrower.

df_melted = pd.melt(df, id_vars=['Name'], value_vars=['Math', 'Science'], var_name='Subject', value_name='Score')
print(df_melted)

You can convert a DataFrame to a dictionary using the to_dict() method.

df_dict = df.to_dict()
print(df_dict)

applymap() is used to apply a function to every element of a DataFrame. It's useful when you want to transform or format all values in the DataFrame.

df = df.applymap(lambda x: round(x, 2))
print(df)