merge two dataframes based on column
df_outer = pd.merge(df1, df2, on='id', how='outer') #here id is common column
df_outermerge two dataframes based on column
df_outer = pd.merge(df1, df2, on='id', how='outer') #here id is common column
df_outerpd merge on multiple columns
new_df = pd.merge(A_df, B_df, how='left', left_on=['A_c1','c2'], right_on = ['B_c1','c2'])pandas merge multiple dataframes
import pandas as pd
from functools import reduce
# compile the list of dataframes you want to merge
data_frames = [df1, df2, df3]
df_merged = reduce(lambda left,right: pd.merge(left,right,on=['key_col'],
how='outer'), data_frames)combine dataframes with two matching columns
merged_df = DF2.merge(DF1, how = 'inner', on = ['date', 'hours'])pandas merge two columns from different dataframes
#suppose you have two dataframes df1 and df2, and
#you need to merge them along the column id
df_merge_col = pd.merge(df1, df2, on='id')python add multiple columns to pandas dataframe
# Basic syntax:
df[['new_column_1_name', 'new_column_2_name']] = pd.DataFrame([[np.nan, 'word']], index=df.index)
# Where the columns you're adding have to be pandas dataframes
# Example usage:
# Define example dataframe:
import pandas as pd
import numpy as np
df = pd.DataFrame({
'col_1': [0, 1, 2, 3],
'col_2': [4, 5, 6, 7]
})
print(df)
col_1 col_2
0 0 4
1 1 5
2 2 6
3 3 7
# Add several columns simultaneously:
df[['new_col_1', 'new_col_2', 'new_col_3']] = pd.DataFrame([[np.nan, 42, 'wow']], index=df.index)
print(df)
col_1 col_2 new_col_1 new_col_2 new_col_3
0 0 4 NaN 42 wow
1 1 5 NaN 42 wow
2 2 6 NaN 42 wow
3 3 7 NaN 42 wow
# Note, this isn't much more efficient than simply doing three
# separate assignments, e.g.:
df['new_col_1'] = np.nan
df['new_col_2'] = 42
df['new_col_3'] = 'wow'Copyright © 2021 Codeinu
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