Key Panda functions

Function	Description	Example
pd.DataFrame()	Creates a DataFrame, a 2D labeled data structure, from a variety of inputs like dictionaries or arrays.	import pandas as pd data = {'A': [1, 2], 'B': [3, 4]} df = pd.DataFrame(data) print(df)
pd.read_csv()	Reads a CSV file and creates a DataFrame.	df = pd.read_csv('file.csv') print(df.head())
df.head()	Displays the first n rows of the DataFrame (default is 5).	print(df.head(3))
df.tail()	Displays the last n rows of the DataFrame (default is 5).	print(df.tail(2))
df.info()	Provides a summary of the DataFrame, including column data types and non-null values.	df.info()
df.describe()	Generates descriptive statistics for numeric columns.	print(df.describe())
df.isnull()	Returns a DataFrame indicating where null values are present.	print(df.isnull())
df.fillna()	Replaces NaN values with a specified value.	df = df.fillna(0) print(df)
df.dropna()	Removes rows or columns with NaN values.	df = df.dropna() print(df)
df.loc[]	Accesses a group of rows and columns by labels or boolean array.	print(df.loc[0, 'A'])
df.iloc[]	Accesses rows and columns by integer index positions.	print(df.iloc[0, 1])
df.sort_values()	Sorts the DataFrame by a specified column or columns.	df = df.sort_values(by='A') print(df)
df.groupby()	Groups DataFrame rows based on a column and allows for aggregation.	grouped = df.groupby('A').sum() print(grouped)
df.merge()	Merges two DataFrames on a key or keys.	df1.merge(df2, on='key')
df.concat()	Concatenates DataFrames along a specified axis.	pd.concat([df1, df2], axis=0)
df.pivot()	Creates a pivot table based on unique values of columns.	pivot = df.pivot(index='A', columns='B', values='C') print(pivot)
df.apply()	Applies a function along an axis (row-wise or column-wise).	df['A'] = df['A'].apply(lambda x: x*2) print(df)
df.value_counts()	Returns a Series with the count of unique values.	print(df['A'].value_counts())
df.corr()	Computes the pairwise correlation of columns in the DataFrame.	print(df.corr())
pd.to_datetime()	Converts a column or series to a datetime object.	df['date'] = pd.to_datetime(df['date']) print(df)
df.plot()	Generates plots for visualizing DataFrame or Series data (requires Matplotlib).	df['A'].plot(kind='line') plt.show()
df.to_csv()	Writes a DataFrame to a CSV file.	df.to_csv('output.csv', index=False)
df.drop()	Removes specified labels from rows or columns.	df = df.drop(columns=['A']) print(df)
df.rename()	Renames columns or indices.	df = df.rename(columns={'A': 'New_A'}) print(df)
df.duplicated()	Checks for duplicate rows in the DataFrame.	print(df.duplicated())
df.nunique()	Counts the number of unique values in each column.	print(df.nunique())
pd.cut()	Bins continuous values into discrete intervals.	df['bins'] = pd.cut(df['A'], bins=3) print(df)
pd.get_dummies()	Converts categorical variables into dummy/indicator variables.	df = pd.get_dummies(df, columns=['Category']) print(df)