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Some useful commands


  • df is the dataframe that you are using (i.e., the table of data that you are analyzing)
  • new_df is the target variable that stores the result of your actions.

Reducing a dataframe to a set of columns that are of interest

new_df = df.loc[:, ['COL_1_NAME', 'COL_2_NAME', 'COL_N_NAME']]

Filtering a dataframe

  • new_df = df[df['COL_NAME']=='VALUE'] - filtering on string values
  • new_df = df[df['COL_NAME']==VALUE] - filtering on numeric values
  • new_df = df_1[df_1['COL_NAME'].isin(df_2['COL_NAME'])] - filtering on the values of a column in another table

Display a dataframe

  • df.head() shows the start of the dataframe
  • df.tail() shows the end of the dataframe
  • df.sample(n) shows a random sample of n rows

Exploratory data analysis

  • df.describe() shows descriptive statistics of the whole dataframe
  • df['COL_NAME'].describe() shows descriptive statistics of one column of the dataframe
  • df[['COL_1_NAME','COL_2_NAME']].describe() shows descriptive statistics of one column of the dataframe

Alternatively, you can ask for specific statistics:

  • df['COL_NAME'].mean() shows the mean of a column
  • You can replace mean with min, max, unique, etc.

Changing column types

Sometimes you want to adjust the type of the values in a column. The following command changes the column type to int.

df['COL_NAME'] = df['COL_NAME'].astype(int)

Simple plotting

A very quick look at the data often reveals important insights. The following command displays a scatterplot.

plt.scatter(df['COL_X_NAME'], df['COL_Y_NAME'])

Faceted Bar charts plotting

chart = alt.Chart(data).mark_bar(stroke='transparent').encode(
    x=alt.X('<COL_NAME_1>', axis=alt.Axis(title='<Title>')),
    y=alt.Y('<COL_NAME_2>', scale=alt.Scale(domain=(<min>, <max>)), axis=alt.Axis(title='<Title>')),

The column command allows you to group the bar chart.

Combining dataframes

Combining dataframes requires a shared column in both dataframes. The following command left-joins df1 and df2.

new_df = pd.merge(df1, df2, on='SHARED_COL_NAME', how='left')

Analysis of grouped data

You can generate descriptive statistics on group data. The following command groups the data on the values of COL_NAME and executes function on the groups:


The following example shows the group-specific means for COL_NAME:


The following addendum sorts grouped data:

[..].sort_values('SORTING_COL', ascending=[True/False])

The following command allows you to apply multiple functions to multiple columns:

new_df = df.groupby(['COL_NAME_1','COL_NAME_2']).agg({'COL_NAME_1':'function','COL_NAME_2':'function'})

You can replace function with mean with min, max, unique, etc.

The following command allows you to count the number of occurences in a group.

new_df = df.groupby('COL_NAME_1')['COL_NAME_2'].value_counts().reset_index(name='count')

This tells you how often COL_NAME_2 appears in each group of COL_NAME_1.

Create dummy variables

Dummy variables help to understand the role of categorial variable. We can store the dummies back into the a dataframe.

new_df = df.join(df['COL_NAME'].str.get_dummies())

Renaming values in a column

df['COL_NAME'] = df['COL_NAME'].map({VALUE_1: NEW_VALUE_1, VALUE_2: NEW_VALUE_2})

Use '' to denote string variables (e.g., 'NEW_VALUE_1')

Creating a pivot table

new_df = df.pivot_table(index=['COL_NAME_1','COL_NAME_2'], columns='KEY', values='AGGREGATE', aggfunc='sum').reset_index()
  • index means the column(s) that become the pivot index.
  • columns means the column(s) that become the keyed columns.
  • values means the column(s) that contain the values of the pivot table.

Row-wise operations

Average between two subsequent rows

df['mean'] = (df['value'] + df['value'].shift(1))/2

Growth-rate between two subsequent rows

df['rate'] = df['value'].pct_change(1)

Convert formatted numbers into float values

Remove the ,

df['value'] = df['value'].str.replace(',','')

Change the type of the column

df['value'] = df['value'].astype(float)
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