Note however that I've first set the index of the df1, df2, df3 to use the variables (foo, bar, etc) rather than the default integers.
import pandas as pd
df1 = pd.DataFrame({
'head1': ['foo', 'bix', 'bar'],
'val': [11, 22, 32]
})
df2 = pd.DataFrame({
'head2': ['foo', 'xoo', 'bar', 'qux'],
'val': [1, 2, 3, 10]
})
df3 = pd.DataFrame({
'head3': ['xoo', 'bar', ],
'val': [20, 100]
})
df1 = df1.set_index('head1')
df2 = df2.set_index('head2')
df3 = df3.set_index('head3')
df = pd.concat([df1, df2, df3], axis = 1)
columns = ['head1', 'head2', 'head3']
df.columns = columns
print(df)
head1 head2 head3
bar 32 3 100
bix 22 NaN NaN
foo 11 1 NaN
qux NaN 10 NaN
xoo NaN 2 20Use a specific index (in the case of DataFrame) or indexes (in the case of Panel or future higher dimensional objects), i.e. the join_axes argument,The data alignment here is on the indexes (row labels). This same behavior can be achieved using merge plus additional arguments instructing it to use the indexes:,pandas provides various facilities for easily combining together Series, DataFrame, and Panel objects with various kinds of set logic for the indexes and relational algebra functionality in the case of join / merge-type operations.,join_axes : list of Index objects. Specific indexes to use for the other n - 1 axes instead of performing inner/outer set logic.
In[1]: df1 = pd.DataFrame({
'A': ['A0', 'A1', 'A2', 'A3'],
...: 'B': ['B0', 'B1', 'B2', 'B3'],
...: 'C': ['C0', 'C1', 'C2', 'C3'],
...: 'D': ['D0', 'D1', 'D2', 'D3']
},
...: index = [0, 1, 2, 3])
...:
In[2]: df2 = pd.DataFrame({
'A': ['A4', 'A5', 'A6', 'A7'],
...: 'B': ['B4', 'B5', 'B6', 'B7'],
...: 'C': ['C4', 'C5', 'C6', 'C7'],
...: 'D': ['D4', 'D5', 'D6', 'D7']
},
...: index = [4, 5, 6, 7])
...:
In[3]: df3 = pd.DataFrame({
'A': ['A8', 'A9', 'A10', 'A11'],
...: 'B': ['B8', 'B9', 'B10', 'B11'],
...: 'C': ['C8', 'C9', 'C10', 'C11'],
...: 'D': ['D8', 'D9', 'D10', 'D11']
},
...: index = [8, 9, 10, 11])
...:
In[4]: frames = [df1, df2, df3]
In[5]: result = pd.concat(frames)pd.concat(objs, axis = 0, join = 'outer', join_axes = None, ignore_index = False,
keys = None, levels = None, names = None, verify_integrity = False,
copy = True)In[6]: result = pd.concat(frames, keys = ['x', 'y', 'z'])In[7]: result.loc['y']
Out[7]:
A B C D
4 A4 B4 C4 D4
5 A5 B5 C5 D5
6 A6 B6 C6 D6
7 A7 B7 C7 D7frames = [process_your_file(f) for f in files] result = pd.concat(frames)
Pandas Removing Duplicate Rows When Merging Two CSV's with Different Dimensions,Find duplicate rows among different groups with pandas,removing duplicate rows in pandas with multiple conditions,removing duplicate rows pandas with condition
Can you not just set all applicable values of amt_received to 0 after the merge?
merged = pd.merge(df_td, df_ld, how = 'inner', on = ['cust_id', 'store_num'])
merged.loc[merged.type_y != 'Received', 'amt_received'] = 0
cust_id nxt_date store_num amt_received type_x trans_id bus_date type_y
0 111111 11 / 5 / 2017 104 0.0 1 10 / 5 / 2017 Payment
1 111111 11 / 5 / 2017 104 0.0 2 10 / 5 / 2017 Payment
2 111111 11 / 5 / 2017 104 10.0 3 10 / 5 / 2017 ReceivedSome of the most interesting studies of data come from combining different data sources. These operations can involve anything from very straightforward concatenation of two different datasets, to more complicated database-style joins and merges that correctly handle any overlaps between the datasets. Series and DataFrames are built with this type of operation in mind, and Pandas includes functions and methods that make this sort of data wrangling fast and straightforward.,Here we'll take a look at simple concatenation of Series and DataFrames with the pd.concat function; later we'll dive into more sophisticated in-memory merges and joins implemented in Pandas.,The combination of options of the pd.concat function allows a wide range of possible behaviors when joining two datasets; keep these in mind as you use these tools for your own data.,Because direct array concatenation is so common, Series and DataFrame objects have an append method that can accomplish the same thing in fewer keystrokes. For example, rather than calling pd.concat([df1, df2]), you can simply call df1.append(df2):
import pandas as pd
import numpy as npdef make_df(cols, ind):
""
"Quickly make a DataFrame"
""
data = {
c: [str(c) + str(i) for i in ind]
for c in cols
}
return pd.DataFrame(data, ind)
# example DataFrame
make_df('ABC', range(3))class display(object):
"""Display HTML representation of multiple objects"""
template = """<div style="float: left; padding: 10px;">
<p style='font-family:"Courier New", Courier, monospace'>{0}</p>{1}
</div>"""
def __init__(self, *args):
self.args = args
def _repr_html_(self):
return '\n'.join(self.template.format(a, eval(a)._repr_html_())
for a in self.args)
def __repr__(self):
return '\n\n'.join(a + '\n' + repr(eval(a))
for a in self.args)
x = [1, 2, 3] y = [4, 5, 6] z = [7, 8, 9] np.concatenate([x, y, z])
array([1, 2, 3, 4, 5, 6, 7, 8, 9])x = [ [1, 2], [3, 4] ] np.concatenate([x, x], axis = 1)
Using pandas.concat() method you can combine/merge two or more series into a DataFrame (create DataFrame from multiple series). Besides this you can also use Series.append(), pandas.merge(), DataFrame.join() to merge multiple Series to create DataFrame.,By using pandas.concat() you can combine pandas objects for example multiple series along a particular axis (column-wise or row-wise) to create a DataFrame.,When you combine two pandas Series into a DataFrame, it creates a DataFrame with the two columns. In this aritcle I will explain different ways to combine two and more Series into a DataFrame.,You can also use DataFrame.join() to join two series. In order to use DataFrame object first you need to have a DataFrame object. One way to get is by creating a DataFrame from Series and use it to combine with another Series.
concat() method takes several params, for our scenario we use list that takes series to combine and axis=1 to specify merge series as columns instead of rows. Note that using axis=0 appends series to rows instead of columns.
import pandas as pd # Create pandas Series courses = pd.Series(["Spark", "PySpark", "Hadoop"]) fees = pd.Series([22000, 25000, 23000]) discount = pd.Series([1000, 2300, 1000]) # Combine two series. df = pd.concat([courses, fees], axis = 1) # It also supports to combine multiple series. df = pd.concat([courses, fees, discount], axis = 1) print(df)
0 1 2 0 Spark 22000 1000 1 PySpark 25000 2300 2 Hadoop 23000 1000
Note that if Series doesn’t contains names and by not proving names to columns while merging, it assigns numbers to columns.
# Create Series by assigning names courses = pd.Series(["Spark", "PySpark", "Hadoop"], name = 'courses') fees = pd.Series([22000, 25000, 23000], name = 'fees') discount = pd.Series([1000, 2300, 1000], name = 'discount') df = pd.concat([courses, fees, discount], axis = 1) print(df)
# Assign Index to Series
index_labels = ['r1', 'r2', 'r3']
courses.index = index_labels
fees.index = index_labels
discount.index = index_labels
# Concat Series by Changing Names
df = pd.concat({
'Courses': courses,
'Course_Fee': fees,
'Course_Discount': discount
}, axis = 1)
print(df)Finally, let’s see how to rest an index using reset_index() method. This moves the current index as a column and adds a new index to a combined DataFrame.
#change the index to a column & create new index
df = df.reset_index()
print(df)