I'm using Python Pandas to merge two dataframe, like so:
new_df = pd.merge(df1, df2, 'inner', left_on='Zip_Code', right_on='Zip_Code_List')
However, I would like to do this ONLY where another column ('Business_Name') in df2 contains a certain value. How do I do this? So, something like, "When Business Name is Walmart, merge these two dataframes."
#you can filter on df2 first and then merge.
pd.merge(df1, df2.query("Business_Name' == 'Walmart'"), how='inner', left_on='Zip_Code', right_on='Zip_Code_List')
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How do I join together 4 DataFrames? The names of the DataFrames are called df1, df2, df3, and df4.
They are all the same column size and I am trying to use the 'inner' join.
How would I modify this code to make it work for all four?
I tried using this code and it worked to combine two of them, but I could not figure out how to write it to work for all four DataFrames.
dfJoin = df1.join(df2,how='inner')
print(dfJoin)
You just have to chain together the joins.
dfJoin = df1.join(df2, how="inner", on="common_column") /
.join(df3, how="inner", on="common_column") /
.join(df4, how="inner", on="common_column")
or if you have more than 4, just put them in a list df_list and iterate through it.
Joining or appending multiple DataFrames in one go can be down with pd.concat():
list_of_df = [df1, df2, df3, df4]
df = pd.concat(list_of_df, how=“inner”)
Your question does now state if you want them merged column- or index-wise, but since your state that they have the same number of columns, I assume you wish to append said DataFrames. For this case the code above works. If you want to make a wide DataFrame, change the attribute axisto 1.
I have two CSV files, CSV_Cleaned: It has 891 rows and CSV_Uncleaned: this one has 945 rows, I wish to get only those rows from CSV_Uncleaned whose index value matches with CSV_Cleaned. How do I do it?
NOTE: My data frame has no column named 'index', I am talking about the index values that are automatically generated on the left of the 1st column.
assuming the column of interest is called "index" on the csv files, you can do this using merge
df1 = pd.read_csv('CSV_cleaned.csv')
df2 = pd.read_csv('CSV_Uncleaned.csv')
df = df1.merge(df2, left_on='index', right_on='index', how='left')
in case you already have DataFrames that need to be merged by their index:
df = df1.merge(df2, left_index=True, right_index=True, how='left')
Basically, I have two dataframes, the first one looks like this:
And the second one like this:
I want to get the columns "lat" and "lnt" of the second one and add to the first one only if the name of the city matches in both dataframes. I tried using pd.merge(), but it's creating new rows with duplicated values.
If possible, I would like to put a NaN in the rows which didn't have any match at all, but I don't want to remove nor add rows to the original dataframe.
The Pandas merge function defaults to an inner join. Since you're looking to merge in the columns of df2 to df1, you should use a left join. This will give you all the rows of df1, and the matching values from df2.
df3 = df1.merge(df2, on = 'city', how = 'left')
merged_df = df1.merge(df2, how = 'inner', on = ['City'])
Welcome, I have a simple question, to which I haven't found a solution.
I have two dataframes df1 and df2:
df1 contains several columns and a multiindex as year-month-week
df2 contains the multiindex year-week with only one column in the df.
I would like to create an inner join of df1 and df2, joining on 'year' and 'week'.
I have tried to do the following:
df1['newcol'] = df1.index.get_level_values(2).map(lambda x: df2.newcol[x])
Which only joins on month (or year?), is there any way to expand it so that the merge is actually right?
Thanks in advance!
df1
df2
Eventually i solved with with removing the multiindex and doing a good old inner join on the two columns and then recreating the multiindex at the end.
Here are the sniplets:
df=df.reset_index()
df2=df2.reset_index()
df['year']=df['year'].apply(int)
df2['year']=df2['year'].apply(int)
df['week']=df['week'].apply(int)
df2['week']=df2['week'].apply(int)
result = pd.merge(df, df2, how='left', left_on= ['year','week'],right_on= ['year','week'])
result=result.set_index(['year', 'month','week','day'])
I am trying to merge two dataframes (call them DF1 & DF2) that basically look like the below. My goal is:
I want open/close/low/high to all come from DF1.
I want numEvents and Volume = DF1 + DF2.
In cases where DF2 has rows that don't exist in DF1, I want open/close/low/high to be NaN (so I can later backfill them), and numEvents and Volume to come from DF2 as is.
Any help is much appreciated!
use pd.merge:
it's outer join since you want data from both dfs.
pd.merge([A,B],how='outer', on=<mutual_key>)
Use the left_on and right_on attributes of pd.merge(). You choose the fields that you want to merge.
DF1.merge(DF2, how='outer', right_on=<keys>...)