- Use .head(), .tail(), .shape and .columns to explore your DataFrame and find out the number of rows and columns as well as the column names.
- Look for NaN (not a number) values with .findna() and consider using .dropna() to clean up your DataFrame.
- You can access entire columns of a DataFrame using the square bracket notation: df['column name'] or df[['column name 1', 'column name 2', 'column name 3']]
- You can access individual cells in a DataFrame by chaining square brackets df['column name'][index] or using df['column name'].loc[index]
- The largest and smallest values, as well as their positions, can be found with methods like .max(), .min(), .idxmax() and .idxmin()
- You can sort the DataFrame with .sort_values() and add new columns with .insert()
- To create an Excel Style Pivot Table by grouping entries that belong to a particular category use the .groupby() method
- used .groupby() to explore the number of posts and entries per programming language
- converted strings to Datetime objects with to_datetime() for easier plotting
- reshaped our DataFrame by converting categories to columns using .pivot()
- used .count() and isna().values.any() to look for NaN values in our DataFrame, which we then replaced using .fillna()
- created (multiple) line charts using .plot() with a for-loop
- styled our charts by changing the size, the labels, and the upper and lower bounds of our axis.
- added a legend to tell apart which line is which by colour
- smoothed out our time-series observations with .rolling().mean() and plotted them to better identify trends over time.
- use HTML Markdown in Notebooks, such as section headings # and how to embed images with the
tag.
- combine the groupby() and count() functions to aggregate data
- use the .value_counts() function
- slice DataFrames using the square bracket notation e.g., df[:-2] or df[:10]
- use the .agg() function to run an operation on a particular column
- rename() columns of DataFrames
- create a line chart with two separate axes to visualise data that have different scales.
- create a scatter plot in Matplotlib
- work with tables in a relational database by using primary and foreign keys
- .merge() DataFrames along a particular column
- create a bar chart with Matplotlib
- How to use .describe() to quickly see some descriptive statistics at a glance.
- How to use .resample() to make a time-series data comparable to another by changing the periodicity.
- How to work with matplotlib.dates Locators to better style a timeline (e.g., an axis on a chart).
- How to find the number of NaN values with .isna().values.sum()
- How to change the resolution of a chart using the figure's dpi
- How to create dashed '--' and dotted '-.' lines using linestyles
- How to use different kinds of markers (e.g., 'o' or '^') on charts.
- Fine-tuning the styling of Matplotlib charts by using limits, labels, linewidth and colours (both in the form of named colours and HEX codes).
- Using .grid() to help visually identify seasonality in a time series.
- Pull a random sample from a DataFrame using .sample()
- How to find duplicate entries with .duplicated() and .drop_duplicates()
- How to convert string and object data types into numbers with .to_numeric()
- How to use plotly to generate beautiful pie, donut, and bar charts as well as box and scatter plots
- Create arrays manually with np.array()
- Generate arrays using .arange(), .random(), and .linspace()
- Analyse the shape and dimensions of a ndarray
- Slice and subset a ndarray based on its indices
- Do linear algebra like operations with scalars and matrix multiplication
- Use NumPys broadcasting to make ndarray shapes compatible
- Manipulate images in the form of ndarrays
- Use nested loops to remove unwanted characters from multiple columns
- Filter Pandas DataFrames based on multiple conditions using both .loc[] and .query()
- Create bubble charts using the Seaborn Library
- Style Seaborn charts using the pre-built styles and by modifying Matplotlib parameters
- Use floor division (i.e., integer division) to convert years to decades
- Use Seaborn to superimpose a linear regressions over our data
- Make a judgement if our regression is good or bad based on how well the model fits our data and the r-squared metric
- Run regressions with scikit-learn and calculate the coefficients.
- How to uncover and investigate NaN values.
- How to convert objects and string data types to numbers.
- Creating donut and bar charts with plotly.
- Create a rolling average to smooth out time-series data and show a trend.
- How to use .value_counts(), .groupby(), .merge(), .sort_values() and .agg().
- Create a Choropleth to display data on a map.
- Create bar charts showing different segments of the data with plotly.
- Create Sunburst charts with plotly.
- Use Seaborn's .lmplot() and show best-fit lines across multiple categories using the row, hue, and lowess parameters.
- Understand how a different picture emerges when looking at the same data in different ways (e.g., box plots vs a time series analysis).
- See the distribution of our data and visualise descriptive statistics with the help of a histogram in Seaborn.
- How to use histograms to visualise distributions
- How to superimpose histograms on top of each other even when the data series have different lengths
- How to use a to smooth out kinks in a histogram and visualise a distribution with a Kernel Density Estimate (KDE)
- How to improve a KDE by specifying boundaries on the estimates
- How to use scipy and test for statistical significance by looking at p-values.
- How to highlight different parts of a time series chart in Matplotib.
- How to add and configure a Legend in Matplotlib.
- Use NumPy's .where() function to process elements depending on a condition.
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