import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3]}, index=['x', 'y', 'z'])
print(df.at['y', 'A'])
2
#24.
df.iat[]Accesses a single value by row/column integer position. Faster than
.iloc.import pandas as pd
df = pd.DataFrame({'A': [10, 20, 30]})
print(df.iat[1, 0])
20
#25.
df.sample()Returns a random sample of items from an axis of object.
import pandas as pd
df = pd.DataFrame({'A': range(10)})
print(df.sample(n=3))
A
8 8
2 2
5 5
(Note: Output rows will be random)
---
#DataAnalysis #Pandas #DataCleaning #Manipulation
Part 3: Pandas - Data Cleaning & Manipulation
#26.
df.dropna()Removes missing values.
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, np.nan, 3]})
print(df.dropna())
A
0 1.0
2 3.0
#27.
df.fillna()Fills missing (NA/NaN) values using a specified method.
import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, np.nan, 3]})
print(df.fillna(0))
A
0 1.0
1 0.0
2 3.0
#28.
df.astype()Casts a pandas object to a specified dtype.
import pandas as pd
df = pd.DataFrame({'A': [1.1, 2.7, 3.5]})
df['A'] = df['A'].astype(int)
print(df)
A
0 1
1 2
2 3
#29.
df.rename()Alters axes labels.
import pandas as pd
df = pd.DataFrame({'a': [1], 'b': [2]})
df_renamed = df.rename(columns={'a': 'A', 'b': 'B'})
print(df_renamed)
A B
0 1 2
#30.
df.drop()Drops specified labels from rows or columns.
import pandas as pd
df = pd.DataFrame({'A': [1], 'B': [2], 'C': [3]})
df_dropped = df.drop(columns=['B'])
print(df_dropped)
A C
0 1 3
#31.
pd.to_datetime()Converts argument to datetime.
import pandas as pd
s = pd.Series(['2023-01-01', '2023-01-02'])
dt_s = pd.to_datetime(s)
print(dt_s)
0 2023-01-01
1 2023-01-02
dtype: datetime64[ns]
#32.
df.apply()Applies a function along an axis of the DataFrame.
import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3]})
df['B'] = df['A'].apply(lambda x: x * 2)
print(df)
A B
0 1 2
1 2 4
2 3 6
#33.
df['col'].map()Maps values of a Series according to an input mapping or function.
import pandas as pd
df = pd.DataFrame({'Gender': ['M', 'F', 'M']})
df['Gender_Full'] = df['Gender'].map({'M': 'Male', 'F': 'Female'})
print(df)
Gender Gender_Full
0 M Male
1 F Female
2 M Male
#34.
df.replace()Replaces values given in
to_replace with value.import pandas as pd
df = pd.DataFrame({'Score': [10, -99, 15, -99]})
df_replaced = df.replace(-99, 0)
print(df_replaced)
Score
0 10
1 0
2 15
3 0
#35.
df.duplicated()Returns a boolean Series denoting duplicate rows.
import pandas as pd
df = pd.DataFrame({'A': [1, 2, 1], 'B': ['a', 'b', 'a']})
print(df.duplicated())
0 False
1 False
2 True
dtype: bool
#36.
df.drop_duplicates()Returns a DataFrame with duplicate rows removed.
import pandas as pd
df = pd.DataFrame({'A': [1, 2, 1], 'B': ['a', 'b', 'a']})
print(df.drop_duplicates())
A B
0 1 a
1 2 b
#37.
df.sort_values()Sorts by the values along either axis.
import pandas as pd
df = pd.DataFrame({'Age': [25, 22, 30]})
print(df.sort_values(by='Age'))
Age
1 22
0 25
2 30
#38.
df.sort_index()Sorts object by labels (along an axis).
import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3]}, index=[10, 5, 8])
print(df.sort_index())
A
5 2
8 3
10 1
#39.
pd.cut()Bins values into discrete intervals.
import pandas as pd
ages = pd.Series([22, 35, 58, 8, 42])
age_bins = pd.cut(ages, bins=[0, 18, 35, 60], labels=['Child', 'Adult', 'Senior'])
print(age_bins)
0 Adult
1 Adult
2 Senior
3 Child
4 Senior
dtype: category
Categories (3, object): ['Child' < 'Adult' < 'Senior']
#40.
pd.qcut()Quantile-based discretization function (bins into equal-sized groups).
import pandas as pd
data = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
quartiles = pd.qcut(data, 4, labels=False)
print(quartiles)
0 0
1 0
2 0
3 1
4 1
5 2
6 2
7 3
8 3
9 3
dtype: int64
#41.
s.str.contains()Tests if a pattern or regex is contained within a string of a Series.
import pandas as pd
s = pd.Series(['apple', 'banana', 'apricot'])
print(s[s.str.contains('ap')])
0 apple
2 apricot
dtype: object
#42.
s.str.split()Splits strings around a given separator/delimiter.
import pandas as pd
s = pd.Series(['a_b', 'c_d'])
print(s.str.split('_', expand=True))
0 1
0 a b
1 c d
#43.
s.str.lower()Converts strings in the Series to lowercase.
import pandas as pd
s = pd.Series(['HELLO', 'World'])
print(s.str.lower())
0 hello
1 world
dtype: object
#44.
s.str.strip()Removes leading and trailing whitespace.
import pandas as pd
s = pd.Series([' hello ', ' world '])
print(s.str.strip())
0 hello
1 world
dtype: object
#45.
s.dt.yearExtracts the year from a datetime Series.
import pandas as pd
s = pd.to_datetime(pd.Series(['2023-01-01', '2024-05-10']))
print(s.dt.year)
0 2023
1 2024
dtype: int64
---
#DataAnalysis #Pandas #Grouping #Aggregation
Part 4: Pandas - Grouping & Aggregation
#46.
df.groupby()Groups a DataFrame using a mapper or by a Series of columns.
import pandas as pd
df = pd.DataFrame({'Team': ['A', 'B', 'A', 'B'], 'Points': [10, 8, 12, 6]})
grouped = df.groupby('Team')
print(grouped)
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x...>
#47.
groupby.agg()Aggregates using one or more operations over the specified axis.
import pandas as pd
df = pd.DataFrame({'Team': ['A', 'B', 'A', 'B'], 'Points': [10, 8, 12, 6]})
agg_df = df.groupby('Team').agg(['mean', 'sum'])
print(agg_df)
Points
mean sum
Team
A 11 22
B 7 14
#48.
groupby.size()Computes group sizes.
❤1
import pandas as pd
df = pd.DataFrame({'Team': ['A', 'B', 'A', 'B', 'A']})
print(df.groupby('Team').size())
Team
A 3
B 2
dtype: int64
#49.
groupby.count()Computes the count of non-NA cells for each group.
import pandas as pd
import numpy as np
df = pd.DataFrame({'Team': ['A', 'B', 'A'], 'Score': [1, np.nan, 3]})
print(df.groupby('Team').count())
Score
Team
A 2
B 0
#50.
groupby.mean()Computes the mean of group values.
import pandas as pd
df = pd.DataFrame({'Team': ['A', 'B', 'A', 'B'], 'Points': [10, 8, 12, 6]})
print(df.groupby('Team').mean())
Points
Team
A 11
B 7
#51.
groupby.sum()Computes the sum of group values.
import pandas as pd
df = pd.DataFrame({'Team': ['A', 'B', 'A', 'B'], 'Points': [10, 8, 12, 6]})
print(df.groupby('Team').sum())
Points
Team
A 22
B 14
#52.
groupby.min()Computes the minimum of group values.
import pandas as pd
df = pd.DataFrame({'Team': ['A', 'B', 'A', 'B'], 'Points': [10, 8, 12, 6]})
print(df.groupby('Team').min())
Points
Team
A 10
B 6
#53.
groupby.max()Computes the maximum of group values.
import pandas as pd
df = pd.DataFrame({'Team': ['A', 'B', 'A', 'B'], 'Points': [10, 8, 12, 6]})
print(df.groupby('Team').max())
Points
Team
A 12
B 8
#54.
df.pivot_table()Creates a spreadsheet-style pivot table as a DataFrame.
import pandas as pd
df = pd.DataFrame({'A': ['foo', 'foo', 'bar'], 'B': ['one', 'two', 'one'], 'C': [1, 2, 3]})
pivot = df.pivot_table(values='C', index='A', columns='B')
print(pivot)
B one two
A
bar 3.0 NaN
foo 1.0 2.0
#55.
pd.crosstab()Computes a cross-tabulation of two (or more) factors.
import pandas as pd
df = pd.DataFrame({'A': ['foo', 'foo', 'bar'], 'B': ['one', 'two', 'one']})
crosstab = pd.crosstab(df.A, df.B)
print(crosstab)
B one two
A
bar 1 0
foo 1 1
---
#DataAnalysis #Pandas #Merging #Joining
Part 5: Pandas - Merging & Concatenating
#56.
pd.merge()Merges DataFrame or named Series objects with a database-style join.
import pandas as pd
df1 = pd.DataFrame({'key': ['A', 'B'], 'val1': [1, 2]})
df2 = pd.DataFrame({'key': ['A', 'B'], 'val2': [3, 4]})
merged = pd.merge(df1, df2, on='key')
print(merged)
key val1 val2
0 A 1 3
1 B 2 4
#57.
pd.concat()Concatenates pandas objects along a particular axis.
import pandas as pd
df1 = pd.DataFrame({'A': [1, 2]})
df2 = pd.DataFrame({'A': [3, 4]})
concatenated = pd.concat([df1, df2])
print(concatenated)
A
0 1
1 2
0 3
1 4
#58.
df.join()Joins columns with other DataFrame(s) on index or on a key column.
❤2🎉1