Pandas

Pandas commands for managing data analysis, manipulation, and visualization

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Learn Pandas Deeper

Master Pandas for data analysis and manipulation

pandas

Read CSV

Read a CSV file into a DataFrame

pd.read_csv
pandas
io
csv
pandas

View Top Rows

View the first few rows of a DataFrame

df.head
pandas
dataframe
view
pandas

DataFrame Info

Get concise summary of DataFrame

df.info
pandas
dataframe
summary
pandas

Descriptive Statistics

Generate descriptive statistics of numeric columns

df.describe
pandas
dataframe
statistics
pandas

Select by Label

Select rows and columns by labels

df.loc
pandas
dataframe
loc
pandas

Select by Position

Select rows and columns by integer position

df.iloc
pandas
dataframe
iloc
pandas

Group By

Group DataFrame using one or more columns

df.groupby
pandas
groupby
aggregation
pandas

Merge DataFrames

Combine DataFrames using database-style joins

pd.merge
pandas
merge
join
pandas

Pivot Table

Create a pivot table from DataFrame

pd.pivot_table
pandas
pivot
aggregation
pandas

Drop Missing Values

Remove rows or columns with missing values

df.dropna
pandas
missing
dropna
pandas

Fill Missing Values

Fill NaN values with a specified value

df.fillna
pandas
missing
fillna
pandas

Sort DataFrame

Sort rows by values or index

df.sort_values
pandas
sort
dataframe
pandas

Concatenate DataFrames

Concatenate DataFrames along rows or columns

pd.concat
pandas
concat
combine
pandas

Export to CSV

Save DataFrame to a CSV file

df.to_csv
pandas
export
csv
pandas

Export to Excel

Save DataFrame to an Excel file

df.to_excel
pandas
export
excel
pandas

Convert to Datetime

Convert column to datetime format

pd.to_datetime
pandas
datetime
convert
pandas

Datetime Accessor

Access datetime properties like year, month, day

df["date"].dt
pandas
datetime
accessor
pandas

Plot Data

Plot DataFrame or Series

df.plot
pandas
plot
visualization
pandas

Melt DataFrame

Unpivot DataFrame from wide to long format

pd.melt
pandas
reshape
melt
pandas

Pivot Wider

Reshape data from long to wide format

df.pivot
pandas
reshape
pivot
pandas

String Operations

Vectorized string methods for Series

df["col"].str
pandas
string
text
pandas

Categorical Data

Convert column to categorical dtype

df["col"].astype("category")
pandas
categorical
optimize
pandas

Change Data Type

Convert Series to specified dtype

df.astype
pandas
dtype
convert
pandas

Unique Values

Find unique values in a Series

df["col"].unique
pandas
unique
values
pandas

Value Counts

Count occurrences of each unique value

df["col"].value_counts
pandas
counts
summary
pandas

Rolling Window

Perform rolling window calculations

df.rolling
pandas
rolling
window
pandas

Expanding Window

Calculate expanding statistics

df.expanding
pandas
expanding
window
pandas

Memory Usage

Check DataFrame memory usage

df.memory_usage
pandas
memory
optimize

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