Find centralized, trusted content and collaborate around the technologies you use most.
Learn more about CollectivesStack Internal
Knowledge at work
Bring the best of human thought and AI automation together at your work.
Explore Stack InternalIf both columns are strings, you can concatenate them directly:
df["period"] = df["Year"] + df["quarter"] If one (or both) of the columns are not string typed, you should convert it (them) first,
df["period"] = df["Year"].astype(str) + df["quarter"] ###Beware of NaNs when doing this!
If you need to join multiple string columns, you can use agg:
df['period'] = df[['Year', 'quarter', ...]].agg('-'.join, axis=1) Where "-" is the separator.
If both columns are strings, you can concatenate them directly:
df["period"] = df["Year"] + df["quarter"] If one (or both) of the columns are not string typed, you should convert it (them) first,
df["period"] = df["Year"].astype(str) + df["quarter"] ###Beware of NaNs when doing this!
If you need to join multiple string columns, you can use agg:
df['period'] = df[['Year', 'quarter', ...]].agg('-'.join, axis=1) Where "-" is the separator.
If both columns are strings, you can concatenate them directly:
df["period"] = df["Year"] + df["quarter"] If one (or both) of the columns are not string typed, you should convert it (them) first,
df["period"] = df["Year"].astype(str) + df["quarter"] If you need to join multiple string columns, you can use agg:
df['period'] = df[['Year', 'quarter', ...]].agg('-'.join, axis=1) Where "-" is the separator.
ifIf both columns are strings, you can concatenate them directly:
df["period"] = df["Year"] + df["quarter"] If one (or both) of the columns are not string typed, you should convert it (them) first,
df["period"] = df["Year"].astype(str) + df["quarter"] ###Beware of NaNs when doing this!
If you need to join multiple string columns, you can use agg:
df['period'] = df[['Year', 'quarter', ...]].agg('-'.join, axis=1) Where "-" is the separator.
if both columns are strings, you can concatenate them directly:
df["period"] = df["Year"] + df["quarter"] If one (or both) of the columns are not string typed, you should convert it (them) first,
df["period"] = df["Year"].astype(str) + df["quarter"] ###Beware of NaNs when doing this!
If you need to join multiple string columns, you can use agg:
df['period'] = df[['Year', 'quarter', ...]].agg('-'.join, axis=1) Where "-" is the separator.
If both columns are strings, you can concatenate them directly:
df["period"] = df["Year"] + df["quarter"] If one (or both) of the columns are not string typed, you should convert it (them) first,
df["period"] = df["Year"].astype(str) + df["quarter"] ###Beware of NaNs when doing this!
If you need to join multiple string columns, you can use agg:
df['period'] = df[['Year', 'quarter', ...]].agg('-'.join, axis=1) Where "-" is the separator.
if both columns are strings, you can concatenate them directly:
dataframe["period"]df["period"] = dataframe["Year"]df["Year"] + df["quarter"] If one (or both) of the columns are not string typed, you should convert it (them) first,
df["period"] = df["Year"].mapastype(str) + dataframe["quarter"]df["quarter"] ###Beware of NaNs when doing this!
If you need to join multiple string columns, you can use agg:
df['period'] = df[['Year', 'quarter', ...]].agg('-'.join, axis=1) Where "-" is the separator.
dataframe["period"] = dataframe["Year"].map(str) + dataframe["quarter"] if both columns are strings, you can concatenate them directly:
df["period"] = df["Year"] + df["quarter"] If one (or both) of the columns are not string typed, you should convert it (them) first,
df["period"] = df["Year"].astype(str) + df["quarter"] ###Beware of NaNs when doing this!
If you need to join multiple string columns, you can use agg:
df['period'] = df[['Year', 'quarter', ...]].agg('-'.join, axis=1) Where "-" is the separator.
