CheatSheet In Pandas

-->Indexing and Slicing

--->Indexing a DataFrame

--->Slicing a DataFrame

--->Indexing a Series

--->Slicing a Series

Joining and Merging in Pandas

--->Joining DataFrames

--->Merging DataFrames

--->Joining and Merging with Duplicate Values

Missing Values In Pandas

--->Checking for Missing Values

--->Dealing with Missing Values

--->Interpolating Missing Values

--->Replacing Values

Aggregating Data In Pandas

--->Aggregation Functions

--->Grouping Data

--->Aggregating Grouped Data

--->Pivoting Data

Datetime In Pandas

--->Creating a DatetimeIndex

--->Accessing Components of a Datetime

--->Pivoting Data

Filtering In Pandas

--->Filtering Data

--->Grouping Data

--->Aggregating Grouped Data

--->Pivoting Data

Indexing and Slicing

Indexing and slicing in pandas are used to select a specific subset of data from a DataFrame or Series. Here is a cheat sheet for indexing and slicing in pandas:

Indexing a DataFrame

Description	Syntax
Select a single column	df['column_name']
Select multiple columns	df[['column_name_1', 'column_name_2']]
Select a row by label	df.loc[label]
Select multiple rows by label	df.loc[[label_1, label_2]]
Select a row by integer location	df.iloc[row_number]
Select a scalar value	df.at[row, col] or df.iat[row, col]

Slicing a DataFrame

Description	Syntax
Select rows by index range	df[start:end] or df[start:end:step]
Select rows by label range	df.loc[start:end] or df.loc[start:end:step]
Select rows by boolean mask	df[boolean_mask]

Indexing a Series

Description	Syntax
Select a single value	s[label] or s.at[label]
Select multiple values	s[[label_1, label_2]] or s.loc[[label_1, label_2]]
Select a value by integer location	s.iloc[row_number]

Slicing a Series

Description	Syntax
Select values by index range	s[start:end] or s[start:end:step]
Select values by label range	s.loc[start:end] or s.loc[start:end:step]
Select values by boolean mask	s[boolean_mask]

Note: In the examples above, df and s represent a DataFrame and Series object respectively, label represents an index label, row_number represents a row index number, start and end represent index labels or integers, and boolean_mask represents a boolean Series.

Joining and merging in pandas are used to combine multiple DataFrames into a single DataFrame based on common columns or indexes. Here is a cheat sheet for joining and merging in pandas:

Joining DataFrames

Description	Syntax
Inner join	pd.merge(df1, df2, on='column_name', how='inner')
Left join	pd.merge(df1, df2, on='column_name', how='left')
Right join	pd.merge(df1, df2, on='column_name', how='right')
Outer join	pd.merge(df1, df2, on='column_name', how='outer')

Note: df1 and df2 represent DataFrames to be joined, and column_name represents the common column name.

Merging DataFrames

Description	Syntax
Merge by index	pd.merge(df1, df2, left_index=True, right_index=True)
Merge on a specific column	pd.merge(df1, df2, on='column_name')
Merge on multiple columns	pd.merge(df1, df2, on=['col_name_1', 'col_name_2'])
Merge with different column names	pd.merge(df1, df2, left_on='col1', right_on='col2')
Merge with a suffix	pd.merge(df1, df2, on='column_name', suffixes=['_1', '_2'])

Note: df1 and df2 represent DataFrames to be merged, and column_name represents the common column name.

Joining and Merging with Duplicate Values

Description	Syntax
Merge and keep duplicate values	pd.merge(df1, df2, on='column_name', how='outer', indicator=True)
Merge and drop duplicate values	pd.merge(df1, df2, on='column_name', how='outer').drop_duplicates()

Note: df1 and df2 represent DataFrames to be merged, and column_name represents the common column name.

Concatenating DataFrames

Description	Syntax
Concatenate vertically	pd.concat([df1, df2], axis=0)
Concatenate horizontally	pd.concat([df1, df2], axis=1)

Note: df1 and df2 represent DataFrames to be concatenated.

Missing Values In Pandas

Missing values in pandas are represented as NaN (Not a Number). Dealing with missing data is a common task in data analysis. Here is a cheat sheet for handling missing values in pandas:

Checking for Missing Values

Description	Syntax
Check if there are any missing values in a DataFrame	df.isna().any()
Check the total number of missing values in a DataFrame	df.isna().sum()
Check the percentage of missing values in a DataFrame	df.isna().mean() * 100

Dealing with Missing Values

Description	Syntax
Drop rows with missing values	df.dropna() or df.dropna(axis=0)
Drop columns with missing values	df.dropna(axis=1)
Fill missing values with a scalar value	df.fillna(value)
Fill missing values with the previous or next value	df.fillna(method='ffill') or df.fillna(method='bfill')
Fill missing values with mean, median, or mode	df.fillna(df.mean()) or df.fillna(df.median()) or df.fillna(df.mode().iloc[0])

Note: df represents a DataFrame object.

Interpolating Missing Values

Description	Syntax
Interpolate missing values	df.interpolate() or df.interpolate(method='linear')

Note: df represents a DataFrame object.

Replacing Values

Description	Syntax
Replace a specific value	df.replace(value_to_replace, new_value)
Replace multiple values	df.replace([value1, value2], [new_value1, new_value2])
Replace using a dictionary	df.replace({value1: new_value1, value2: new_value2})

Note: df represents a DataFrame object. value_to_replace, new_value, value1, value2, new_value1, and new_value2 are scalar values.

Aggregating Data In Pandas

Aggregating data involves computing a summary statistic of a dataset or a group of data. Here is a cheat sheet for aggregating data in pandas:

Aggregation Functions

Description	Syntax
Compute the sum of a column	df['column_name'].sum()
Compute the mean of a column	df['column_name'].mean()
Compute the median of a column	df['column_name'].median()
Compute the mode of a column	df['column_name'].mode()
Compute the minimum of a column	df['column_name'].min()
Compute the maximum of a column	df['column_name'].max()
Compute the variance of a column	df['column_name'].var()
Compute the standard deviation of a column	df['column_name'].std()
Compute the count of a column	df['column_name'].count()

Note: df represents a DataFrame object.

Grouping Data

Description	Syntax
Group data by a column	df.groupby('column_name')
Group data by multiple columns	df.groupby(['column_name1', 'column_name2'])

Note: df represents a DataFrame object.

Aggregating Grouped Data

Description	Syntax
Aggregate data using a single function	df.groupby('column_name').agg(function)
Aggregate data using multiple functions	df.groupby('column_name').agg([function1, function2])
Aggregate data using a dictionary	df.groupby('column_name').agg({'column_name1': function1, 'column_name2': function2})

Note: df represents a DataFrame object. column_name represents the column to group by. function represents an aggregation function such as sum, mean, median, etc.

Pivoting Data

Description	Syntax
Pivot data using a single index and a single column	df.pivot(index='index_name', columns='column_name', values='value_name')
Pivot data using multiple indexes and columns	df.pivot_table(index=['index_name1', 'index_name2'], columns=['column_name1', 'column_name2'], values='value_name')

Datetime In Pandas

Working with date and time data is a common task in data analysis. Here is a cheat sheet for working with dates and times in pandas:

Creating a DatetimeIndex

Description	Syntax
Convert a string to a datetime object	pd.to_datetime('2010-01-01')
Convert a list of strings to a DatetimeIndex	pd.to_datetime(['2010-01-01', '2010-01-02', '2010-01-03'])
Create a DatetimeIndex from a range of dates	pd.date_range(start='2010-01-01', end='2010-01-31')

Note: pd represents the pandas module.

Accessing Components of a Datetime

Description	Syntax
Access the year of a datetime	df['date_column'].dt.year
Access the month of a datetime	df['date_column'].dt.month
Access the day of a datetime	df['date_column'].dt.day
Access the hour of a datetime	df['date_column'].dt.hour
Access the minute of a datetime	df['date_column'].dt.minute
Access the second of a datetime	df['date_column'].dt.second

Note: df represents a DataFrame object. date_column represents the name of the column containing datetime values.

Resampling Time Series Data

Description	Syntax
Resample time series data	df.resample(rule).agg(function)

Note: df represents a DataFrame object. rule represents the resampling frequency, such as 'D' for daily or 'M' for monthly. function represents an aggregation function, such as 'mean' or 'sum'.

Shifting Dates

Description	Syntax
Shift dates forward or backward	df['date_column'].shift(periods=n)

Note: df represents a DataFrame object. date_column represents the name of the column containing datetime values. n represents the number of periods to shift the dates by.

Rolling Windows

Description	Syntax
Compute a rolling window	df['column_name'].rolling(window=n).agg(function)

Note: df represents a DataFrame object. column_name represents the name of the column to compute the rolling window on. n represents the size of the rolling window. function represents an aggregation function, such as 'mean' or 'sum'.

These are some common methods for working with dates and times in pandas, but there are many more methods and options available.

Filtering In Pandas

Filtering and grouping data are common tasks in data analysis. Here is a cheat sheet for working with filtering and grouping in pandas:

Filtering Data

Description	Syntax
Filter rows based on a condition	df[df['column_name'] < value]
Filter rows based on multiple conditions	df[(df['column_name1'] < value1) & (df['column_name2'] > value2)]
Filter rows based on a substring	df[df['column_name'].str.contains('substring')]
Filter rows based on missing values	df[df['column_name'].isna()] or df[df['column_name'].notna()]

Note: df represents a DataFrame object. column_name represents the name of the column to filter on. value represents the value to filter by.

Grouping Data

Description	Syntax
Group by a single column	df.groupby('column_name').agg(function)
Group by multiple columns	df.groupby(['column_name1', 'column_name2']).agg(function)
Apply multiple aggregation functions to a group	df.groupby('column_name').agg(['mean', 'max', 'min'])
Group by time intervals	df.groupby(pd.Grouper(key='date_column', freq='D')).agg(function)

Note: function represents an aggregation function, such as 'mean' or 'sum'. pd.Grouper is a pandas object for grouping time intervals. key represents the name of the column containing datetime values. freq represents the frequency of the time interval, such as 'D' for daily or 'M' for monthly.

These are some common methods for working with filtering and grouping in pandas, but there are many more methods and options available.

Here's a quick cheatsheet for some common visualization techniques using the Matplotlib library in Python:

Importing the library

import matplotlib.pyplot as plt

Line plot

plt.plot(x, y)
plt.xlabel('X Label')
plt.ylabel('Y Label')
plt.title('Title')
plt.show()

Scatter plot

plt.scatter(x, y)
plt.xlabel('X Label')
plt.ylabel('Y Label')
plt.title('Title')
plt.show()

Bar plot

plt.bar(x, height)
plt.xlabel('X Label')
plt.ylabel('Y Label')
plt.title('Title')
plt.show()

Histogram

plt.hist(x, bins=10)
plt.xlabel('X Label')
plt.ylabel('Y Label')
plt.title('Title')
plt.show()

Box plot

plt.boxplot(x)
plt.xlabel('X Label')
plt.ylabel('Y Label')
plt.title('Title')
plt.show()

Pie chart

plt.pie(x, labels=labels)
plt.title('Title')
plt.show()

Heatmap

plt.imshow(data, cmap='coolwarm')
plt.colorbar()
plt.title('Title')
plt.show()

Here's a quick cheatsheet for some common visualization techniques using the Seaborn library in Python:

Importing the library

import seaborn as sns

Line plot

sns.lineplot(x=x, y=y, hue=hue, style=style, markers=True)
sns.set_style("whitegrid")
sns.despine(left=True)
plt.xlabel('X Label')
plt.ylabel('Y Label')
plt.title('Title')
plt.show()

Scatter plot

sns.scatterplot(x=x, y=y, hue=hue, style=style, markers=True)
sns.set_style("whitegrid")
sns.despine(left=True)
plt.xlabel('X Label')
plt.ylabel('Y Label')
plt.title('Title')
plt.show()

Bar plot

sns.barplot(x=x, y=y, hue=hue)
sns.set_style("whitegrid")
sns.despine(left=True)
plt.xlabel('X Label')
plt.ylabel('Y Label')
plt.title('Title')
plt.show()

Histogram

sns.histplot(x, kde=True)
sns.set_style("whitegrid")
sns.despine(left=True)
plt.xlabel('X Label')
plt.ylabel('Y Label')
plt.title('Title')
plt.show()

Box plot

sns.boxplot(x=x, y=y, hue=hue)
sns.set_style("whitegrid")
sns.despine(left=True)
plt.xlabel('X Label')
plt.ylabel('Y Label')
plt.title('Title')
plt.show()

Heatmap

sns.heatmap(data, cmap='coolwarm')
plt.title('Title')
plt.show()

Pair plot

sns.pairplot(data, hue=hue)
plt.title('Title')
plt.show()