When the values are numeric and apply operators:

df.groupby(level=0).sum()
df.groupby(df.index).sum()
df[specifiedColumn].groupby(level=0).sum()

When the values are numeric and apply functions:

# Common usage, merging values
df[1].groupby(level=0).apply(lambda xList: ';'.join(list([x for x in xList])))

Source:
https://pandas.pydata.org/pandas-docs/version/0.22/generated/pandas.core.groupby.GroupBy.apply.html
https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.GroupBy.sum.html

Merge multiple row or column values into one row or column:

df.loc[colSelected,].apply(lambda x: ';'.join(x.astype(str)), axis=0)
df.loc[,rowSelected].apply(lambda x: ';'.join(x.astype(str)), axis=1)

Source:
https://stackoverflow.com/questions/33098383/merge-multiple-column-values-into-one-column-in-python-pandas

When you have a table with many columns but only want to rearrange or swap two or a few columns.

cols = list(df.columns)
a, b = cols.index('XXXX'), cols.index('YYYY')
cols[b], cols[a] = cols[a], cols[b]
df = df[cols]

Source:
https://stackoverflow.com/questions/53141240/pandas-how-to-swap-or-reorder-columns

Replace a specific value with another value in a dataframe:

df.replace(originalValue, newValue, inplace=True)
df = df.replace(originalValue, newValue)

Replace values not equal to a given value with another value:

df[df != originalValue] = newValue

Remove rows with the same index, only keep one first or last of them.

df = df.loc[~df.index.duplicated(keep='first')]
df = df.loc[~df.index.duplicated(keep='last')]

Source:
https://stackoverflow.com/questions/13035764/remove-rows-with-duplicate-indices-pandas-dataframe-and-timeseries

Rename only one or a few pandas dataframe columns:

df.rename(columns={"A": "a", "B": "c"}, inplace=True)

Rename one or a few index:

df.rename(index={"A": "a", "B": "c"}, inplace=True)

Source:
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rename.html

Add empty row with or without name:

df.append(pd.Series(name='NameOfNewRow')) # name the new row
df.append(pd.Series(), ignore_index=True) # not name the new row

Add empty column:

df['new'] = pd.Series()

Source:
https://stackoverflow.com/questions/39998262/append-an-empty-row-in-dataframe-using-pandas
https://stackoverflow.com/questions/16327055/how-to-add-an-empty-column-to-a-dataframe

Filter a dataframe by the sum of rows:

df = df[df.sum(axis=1) > 0]
df = df.loc[df.sum(axis=1) > 0,:]

Filter by sum of columns:

df = df.loc[:,df.sum() > 0]
df = df.loc[:,df.sum(axis=0) > 0]

Source:
https://stackoverflow.com/questions/40425484/filter-dataframe-in-pandas-on-sum-of-rows

Remove rows with nan/null/missing values:

df = df.dropna(axis=0, how='any') # Remove if any value is na
df = df.dropna(axis=0, how='all') # Remove if all values are na

Remove columns with nan/null/missing values:

df = df.dropna(axis=1, how='any') # Remove if any value is na
df = df.dropna(axis=1, how='all') # Remove if all values are na

Defaut remove is inplace=False, if you want to remove inplace, add inplace=True

Source:
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.dropna.html

df.isnull().any().any()