Manipulate data

Perform novel analysis on your data.

Manipulating data in Quadratic is easier than ever as you can view your changes in the sheet in real-time. Here is a non-exhaustive list of ways to manipulate your data in Quadratic:

1. Find correlations

2. Basic stats - max, min, average, selections, etc.

3. DataFrame math

4. Data selections

1. Find correlations

# Get the correlation and show the value in the sheet
data['col1'].corr(data['col2'], method='pearson')

# possible methods: pearson, kendall, spearman 

2. Basic stats - max, min, mean, selections, etc.

Reference: https://pandas.pydata.org/docs/getting_started/intro_tutorials/06_calculate_statistics.html

# Get the max value of a column
df["col1"].max()

# Get the min value of a column
df["col1"].min()

# Get the mean value of a column
df["col1"].mean()

# Get the median value of a column
df["col1"].median()

# Get the skew for all columns
df.skew()

# Count the values in a column
df["col1"].value_counts()

# Get the summary of a column
df["col1"].describe()

3. DataFrame math

Do math on data in the DataFrame. Alternatively, use formulas in the sheet on the values.

# Add, subtract, multiply, divide, etc., will all work on all values in a column
df['col1'] + 1
df['col1'] - 1
df['col1'] * 2
df['col1'] / 2 

# Do any arbitrary math column-wise with the above or do DataFrame-wise via
df + 1 

4. Data selections

Alternatively, cut/copy/paste specific values in the sheet.

# get a column 
df['col1'] 

# get multiple columns 
df[['col1', 'col2']] 

# filtering